行业研究动态

Skytopia: Monocular Drone Navigation with Action-Conditioned Latent World Models

World models for robot navigation are normally built to be executed: a prediction is produced at deployment and fed back into action generation at every control step. Skytopia argues that this is the wrong contract — what a policy needs from a world model is the representation required to produce a prediction, not the prediction itself, because in flight the executed action already explains most of the change between observations and prediction reduces to reprojecting a static scene under a known displacement. The policy is therefore built on an action-conditioned latent world model, trained on a purpose-built 3D Gaussian Splatting platform, with a forward objective predicting the next observation's representation from intended motion and an inverse objective recovering that motion from the predicted transition. Because the prediction never reaches action generation, the predictor is discarded at deployment, and one policy serves point-goal, image-goal, and goal-free navigation at 57.8, 66.0, and 49.0 percent success while cutting inference cost by 59.4 percent. The same policy flies on a physical drone without fine-tuning in indoor, open outdoor, and woodland settings.

MATE: Multi-Agent Virtual Teleoperation Platform for Humanoid Collaboration Data Collection

Humanoid robots need embodied experience of collaborative work, but physical multi-robot data collection does not scale: it demands several robots, a dedicated space, and repeated resets. MATE replaces the hardware with a shared physics simulation in which multiple geographically distributed operators simultaneously teleoperate whole-body humanoids, preserving the physically coupled interactions between humanoids, objects, and environments that make collaboration data valuable in the first place. Using MATE the authors build a multi-humanoid collaboration dataset of 24.1 hours across 2,500 joint episodes and five long-horizon tasks, including object handover, relay delivery, environment interaction, and cooperative transport, and introduce EAIS, an execution-aligned interaction sampling strategy that prioritizes task-progressing and interaction-critical behavior. Imitation learning and vision-language-action policies trained on this data learn effectively and transfer zero-shot to a physical humanoid without real-world fine-tuning.

ε4P: Upcycling Imperfect Data for High-Precision Robotic Manipulation

High-precision manipulation is where vision-language-action policies break down, and the usual remedy is task-specific teleoperation data, which is slow and expensive to collect. ε4P attacks the data problem instead of the model: it upcycles two sources that are normally thrown away, namely low-precision data from the target task and high-precision data from mismatched tasks, and controls where each source contributes along the flow-matching trajectory rather than mixing them uniformly. Low-precision target-task data is applied at high noise to preserve high-level task context, while high-precision mismatched-task data is applied at low noise to transfer low-level action precision. On real-robot sub-millimeter precision tasks and coarse-grained tasks, the method improves policy performance by up to 31.7 percentage points and can replace an equal amount of task-specific high-quality data with an average drop of only 4.2 points.

Skytopia：用动作条件隐世界模型实现单目无人机导航

机器人导航中的世界模型通常被设计成「要被执行」的：部署时先产生预测，再在每一个控制步把预测反馈给动作生成。Skytopia 认为这个契约本身是错的——策略真正需要的不是预测结果，而是产生该预测所必需的表示；因为在飞行中，已执行的动作几乎解释了相邻观测之间的全部变化，预测退化为「在已知位移下重投影一个静态场景」。于是策略建立在一个动作条件的隐世界模型之上，并用专门搭建的三维高斯泼溅平台训练：前向目标从意图运动预测下一观测的表示，逆向目标则从预测出的状态转移中恢复该运动。由于预测从不进入动作生成，预测器在部署时被直接丢弃，一个策略同时覆盖点目标、图像目标与无目标导航，成功率分别为 57.8%、66.0% 与 49.0%，同时把推理开销降低 59.4%。同一策略无需微调即可在实体无人机上飞行，覆盖室内、开阔室外与林地环境。

MATE：面向人形机器人协作数据采集的多操作者虚拟遥操作平台

人形机器人需要关于协作的具身经验，但真机多机器人数据采集无法规模化：它需要多台机器人、专用场地，还要反复重置。MATE 用共享物理仿真替代硬件，让多位地理位置分散的操作者同时遥操作全身人形机器人，同时保留了人形机器人、物体与环境之间真实物理耦合的交互——而这恰恰是协作数据价值的来源。借助 MATE，作者构建了包含 24.1 小时、2500 个联合回合、五类长时序任务的多机协作数据集，覆盖物体交接、接力配送、环境交互与协同搬运；并提出 EAIS（执行对齐的交互采样）策略，优先采样推进任务进度与交互关键的行为。基于该数据训练的模仿学习与视觉语言动作策略学习效果良好，并能零样本迁移到实体人形机器人，无需真机微调。

ε4P：把不完美数据「升级再造」为高精度操作能力

高精度操作是视觉语言动作（VLA）策略最容易失手的地方，而它依赖的任务专用高质量数据通常来自遥操作，采集既慢又贵。ε4P 选择从数据侧而非模型侧解题：它把两类通常被直接丢弃的数据「升级再造」——目标任务上的低精度数据，以及任务不匹配的高精度数据——并且不是把它们均匀混进联合训练，而是控制每种数据在流匹配轨迹上的作用区间。低精度的目标任务数据用在高噪声段，保留任务层面的上下文；高精度但任务不匹配的数据用在低噪声段，迁移底层的动作精度。在真实机器人的亚毫米级高精度任务与粗粒度任务上，该方法把策略表现最多提升 31.7 个百分点，并且可以用不完美数据替换等量的任务专用高质量数据，平均仅损失 4.2 个百分点。

PredActor: Predictive Action Diffusion for Steerable Onboard Humanoid Control

Diffusion models generate flexible motion, but turning that flexibility into feedback-responsive humanoid control is awkward: hierarchical systems steer through motion references that a separate tracker may not be able to execute, action-only diffusion has no explicit future-state trajectory to steer, and joint state-action diffusion controllers typically depend on privileged full-body state estimates while leaving behavior selection and test-time steering fragmented. PredActor consolidates these capabilities into one directly executed policy driven by proprioceptive observations: conditioned on proprioceptive history and optional task context, it jointly generates executable actions and an internal future-state trajectory, with classifier-free guidance strengthening text-conditioned behavior and classifier guidance steering predicted states toward test-time objectives. Only the actions are executed, with no separate motion-reference tracker and no externally estimated full-body state as input. In simulation it reaches all 15 destination targets with a text retrieval score of 0.580 against 0.373 for conditional action diffusion; rolling denoising and computation-preserving runtime optimizations bring the complete callback to 16.790 ms median and 19.383 ms p95 on a Jetson Orin NX, both inside the 20 ms control period. It is deployed on a Unitree G1.

DexTacWAM: A Visuo-Tactile World-Action Model for Dexterous Manipulation

Dexterous manipulation rests on contact dynamics that cameras only partially reveal, yet the current generation of World-Action Models couples predictive video with action generation while remaining vision-centric, so the signal that decides success in contact-rich tasks never enters the predicted world state. DexTacWAM encodes each fingertip independently, aggregates the features through a finger- and pose-aware tactile compressor, and injects the resulting tactile latent into a video diffusion world model so contact evolution becomes part of the imagined future. Across six contact-rich tasks on a 22-DoF bimanual platform it scores highest on every task, averaging 70.6 against 38.0 for the strongest baseline; an ablation that removes tactile world modeling while keeping the same tactile features and action expert collapses the four-task mean from 74.7 to 26.6, locating the gain in the world state rather than in tactile conditioning. Adaptation to touch takes roughly four hours and around 100 demonstrations per task with a frozen pretrained vision VAE.

Think Like a World Model, Act Like a VLA: Distilling World-Model Representations into Compact Robot Policies

Vision-Language-Action models map observations to actions with no objective that accounts for how the world actually responds, so their robustness is bounded by data coverage; world models carry exactly that missing objective but cost seconds per decision and cannot sit in a control loop. This paper argues the two can be separated: physical grounding lives in a world model's internal features, while future generation is merely the objective that produced those features, so the grounding can be inherited and the generative machinery discarded. One feature-alignment term is added to ordinary VLA training, with a frozen world model run once over the training frames and cached; no teacher is loaded during training, the projector is dropped afterwards, and the deployed policy is identical to the undistilled baseline at 32 ms and 1.86 GB on a consumer RTX 5090. A 0.8B student reaches 97.9% on LIBERO, lifts RoboCasa-GR1 humanoid manipulation from 48.2% to 50.5%, and transfers to real single-arm and bimanual hardware.

像世界模型一样思考，像 VLA 一样行动：把世界模型表征蒸馏进轻量机器人策略

视觉-语言-动作（VLA）模型把观测映射成动作，训练目标里没有任何一项去刻画「世界会如何回应」，于是它的鲁棒性本质上被数据覆盖范围所限定：分布内可以很好，稍出分布就完全没有对「后果」的概念。世界模型恰好带着这个缺失的目标——它是被训练来预测下一刻会发生什么的，因此也更有物理根据——但把未来推进一步要花上秒级的计算，根本进不了控制回路。这篇文章主张二者本可分离：世界模型对物理场景的认知存放在它的内部特征里，而「生成未来」只是产生这些特征时所用的训练目标。于是接地能力可以被继承，生成机器则可以丢开。做法是在普通 VLA 训练中增加一项特征对齐损失：冻结的世界模型先在训练帧上跑一遍并缓存，学生只需去对齐这份缓存。训练时不加载教师，投影器用完即弃，部署策略与未蒸馏的基线完全相同，在消费级 RTX 5090 上仅 32 ms、1.86 GB。0.8B 的学生模型在 LIBERO 上达到 97.9%，把 RoboCasa-GR1 人形操作从 48.2% 提到 50.5%，并迁移到真实的单臂与双臂平台。

PredActor：可导向的机载人形机器人预测式动作扩散策略

扩散模型能生成灵活多样的动作，但要把这种灵活性变成能响应反馈的人形机器人控制器，中间隔着不少麻烦：分层方案通过动作参考来引导，而参考动作可能超出单独跟踪器的能力，于是恢复与物理执行都落到了跟踪器身上；只生成动作的扩散没有显式的未来状态轨迹可供测试期导向；联合状态-动作扩散虽然提供了这条轨迹，但代表性的控制器依赖特权式的全身状态估计，而行为选择与测试期导向又分散在不同环节里。PredActor 把这些能力收进一个直接用本体感知观测驱动的策略：以本体感知历史与可选任务上下文为条件，联合生成可执行动作和一条内部未来状态轨迹，用无分类器引导强化文本条件下的行为选择，用分类器引导把预测状态导向测试期目标；执行时只执行动作，既不需要单独的动作参考跟踪器，也不把外部估计的全身状态作为策略输入。仿真中它到达全部 15 个目标点，文本检索得分 0.580，而条件式动作扩散为 0.373；配合滚动去噪与保算力的运行时优化，完整回调在 Jetson Orin NX 上达到中位 16.790 ms、p95 19.383 ms，都落在 20 ms 控制周期之内。策略已部署在宇树 G1 上。

DexTacWAM：把接触动力学写进世界模型的视触觉灵巧操作框架

灵巧操作的成败由接触决定，而接触恰恰是相机看不全的部分：指尖压力、起滑瞬间、手指间的载荷转移，视觉几乎无法直接观测。当前的世界动作模型（World-Action Model）把预测式视频生成与动作生成耦合在一起，思路本来对路，但整体仍是「视觉中心」的，于是「预测出来的未来」里根本没有决定任务成败的接触动力学。DexTacWAM 让每个指尖独立编码，再用一个感知手指身份与手部姿态的触觉压缩器做聚合，最后把触觉 latent 注入视频扩散世界模型，使接触演化本身成为被预测的世界状态。在一个 22 自由度的双臂平台上，它在六个强接触任务上每一项都取得最高分，平均 70.6，对最强基线为 38.0；一个消融实验把「触觉世界建模」去掉而保留同样的触觉特征与动作专家，四项任务均值从 74.7 掉到 26.6，说明收益来自「预测接触」而非「用触觉做条件」。适配触觉只需约四小时、每任务约 100 条演示。

LunaDrive: Pushing a Flat BLDC Motor to a 96 V Bus with GaN FETs

How fast a dynamic robot moves is ultimately capped by the rotational speed limit of its actuators, and most commercially available servo drives assume a bus voltage of 48 V or lower, which puts a hard ceiling on instantaneous maximum speed. The obstacle is a property of the power devices themselves: voltage rating and current capacity trade off against each other in semiconductors, so conventional Si MOSFET drivers struggle to deliver both high voltage and high current. GaN FETs are promising, yet compact drivers that can be mounted on the rear side of a flat BLDC motor remain scarce. This work develops a GaN FET driver for high-power flat BLDC motors and studies the effect of delay compensation in the high-speed region beyond the rated operating range. Under 96 V operation the driver achieves 30 A continuous current with a heat sink, a peak current of 80 A, and a maximum electrical frequency of 3110 Hz, and a high-speed load-lifting experiment powered by a 24S (100 V) LiPo battery demonstrates applicability to dynamic robot operation.

Packing the Gripper Into the Plan: Closed-Loop Dense Packing of Irregular Objects

Automatic dense packing is what warehouse operations actually want, and it has stayed largely in simulation: prior work treats the object as an isolated rigid body with idealized contact, inserts the gripper as a discrete post-hoc feasibility check if at all, and leaves perception and contact drift during execution unaddressed. This paper delivers a closed-loop pipeline that runs on real hardware, integrating perception, gripper-aware placement optimization, and force-guided execution. The optimizer represents the object together with the gripper as one composite body of hierarchical sphere trees and searches five degrees of freedom on a GPU inside a CMA-ES framework, with the vertical coordinate grounded analytically against the current heightmap. During execution a force-monitored vertical descent stops on first contact, and a post-release consolidation push closes the residual lateral clearance that gripper-aware planning leaves behind, while the container is re-perceived between placements so drift does not accumulate. The system is validated on a Franka Emika Panda packing 3D-printed flat, curved, and concave objects plus a YCB subset, with an ablation isolating gripper-aware optimization, the consolidation push, and mesh-derived geometry against end-to-end success, achieved density, and computational cost, and a benchmark against a heightmap-minimization baseline representative of prior work.

LIMBO: Learning Safety Into the Policy Instead of Filtering It at Runtime

Safe whole-body control has to hold collision avoidance and balance together under high-dimensional nonlinear dynamics, and safety certificates are typically hand-designed per behavior, then re-derived and re-argued whenever the task changes. LIMBO moves certificate synthesis from design to learning. It learns a state-action control barrier function around a frozen base controller, over residual actions, with a state-based failure specification, which makes Q-CBF synthesis tractable in the full control dimension while placing the certificate in the control space of the task policy. The learned safety value then drives risk-guided sampling near the estimated boundary of recoverability, and later serves as a teacher supplying action-level safety feedback during task learning, so the safety structure is internalized and no online safety filter is needed at deployment. The authors demonstrate the framework on a 29-degree-of-freedom humanoid performing dodgeball avoidance and locomotion beneath low obstacles, with both policies transferring to hardware without online filtering; notably, under a fixed safety specification, varying only the sampling concentration yields strategies ranging from crouching to a novel backward-leaning limbo maneuver.

LunaDrive：把扁平无刷电机的母线电压推到 96V 的 GaN 驱动方案

动态机器人的动作速度最终受限于执行器的转速上限，而市面上多数伺服驱动器假定母线电压不超过 48V，这一约束直接压住了瞬时最高转速。问题在于功率器件本身的取舍：耐压与通流能力在半导体上是相互挤压的指标，传统 Si MOSFET 驱动器很难同时做到高压与大电流；GaN FET 虽被看好，但能贴在扁平无刷电机背面这种紧凑位置的驱动器仍然很少。这篇工作开发了一款面向高功率扁平无刷电机的 GaN FET 驱动器，并研究了超出额定工作范围的高速区间里延迟补偿的效果。实验在 96V 母线电压下实现 30A 连续电流（加装散热片）、80A 峰值电流与 3110 Hz 的最高电频率，并用 24S（100V）锂聚合物电池完成了一次高速举升负载实验，验证其在动态机器人场景下的可用性。

把抓取器算进摆放里：不规则物体的密集码放闭环系统

仓储场景真正想要的密集码放，一直停留在仿真里：既有工作大多把物体当成孤立的刚体，接触被理想化，抓取器往往只是一个事后的可行性检查，执行过程中累积的感知误差与接触漂移也没人管。这篇工作给出了一条在真机上跑通的闭环流水线，把感知、抓取器感知的摆放优化与力引导执行串在一起。优化器把物体和抓取器合成一个由层级球体树表示的复合刚体，在 GPU 上以 CMA-ES 搜索五个自由度，竖直方向则依据当前高度图解析求解；执行阶段监控力的垂直下压会在首次接触时停止，随后一次释放后的压实推压（consolidation push）用来消除抓取器感知规划留下的侧向间隙，而每次摆放之间容器都会被重新感知，使漂移不会累积。作者在 Franka Emika Panda 上码放 3D 打印的平面、曲面与凹面物体以及一部分 YCB 物体，并通过消融实验分别量化了抓取器感知优化、压实推压与网格几何对成功率、实际密度与计算开销的贡献，同时与高度图最小化方法做了对照。

LIMBO：把安全证书学进策略本身，让 29 自由度人形机器人贴着可恢复边界运动

安全全身控制要在高维非线性动力学下同时管住碰撞与平衡，而安全证书（控制屏障函数）通常是为某一类行为手工设计的，换任务就得重新设计、重新验证。LIMBO 把证书的合成从「设计」改成「学习」：在一个冻结的底层控制器周围学习状态-动作控制屏障函数，把安全判据写成基于状态的失败规范，从而在完整的残余动作维度上完成合成；合成阶段学到的安全价值函数还会引导风险采样，主动把样本撒向估计的可恢复边界附近。随后它充当教师，在任务学习阶段给出动作级的安全反馈，安全结构因此被内化进任务策略，部署时不再需要在线安全滤波器。作者在 29 自由度人形机器人上演示了躲闪球与低障碍下穿行两个任务，两者都迁移到真机且不带在线滤波；一个额外的发现是，在同一个安全规范下仅改变采样集中度，就能得到从下蹲到一种新的后仰钻越动作的策略谱。

GeoAAC: Geometry-Based Adaptive Action Chunking for Flow-Matching VLA Policies

Action chunking is standard practice for generating and executing actions in vision-language-action (VLA) policies, yet existing methods use a fixed action horizon. Within a single rollout, different task stages call for different levels of action continuity, control precision and closed-loop feedback, so one fixed horizon cannot accommodate all of them. The authors propose GeoAAC, a geometry-based adaptive action chunking method for flow-based VLA policies that sets the action horizon from the reliability of the current prediction. The observation behind it is that the geometry of flow-matching denoising trajectories carries process-level information about prediction reliability, with geometric variation across action prefixes positively correlated with predictive uncertainty. GeoAAC turns prefix-wise geometry into a horizon-wise geometric profile and determines the action horizon adaptively within a single generation, with no additional training. Experiments with GR00T N1.5 and pi0.5 on LIBERO, LIBERO-Pro, RoboCasa365 and real-world manipulation show consistent gains over fixed-horizon baselines and existing adaptive methods: up to 8.7 percentage points in simulation and real-world average success rising from 53.3% to 74.4%.

SafeHarness: Making Safety a Planning Priority for Coding Agents on Robots

Coding agents are emerging as a route to robot manipulation: a language model writes the controller as a program, and agents built this way operate robots with no robot-specific training. Whether that route is safe had not been asked. The authors pair each manipulation goal with an obstacle the robot must not touch, and find the agent collides with it in most cases. The agent discusses the obstacle in its traces and the prompt already forbids touching it, so the fault lies neither in perception nor in instruction but in planning, where the constraint never becomes a priority. Decomposing manipulation into a route phase and a contact-rich moment localizes the failure: along the route the model has no notion of a clearing route and no replanning once a route becomes infeasible, and at contact it is unaware that execution is bounded by the same constraint. SafeHarness supplies two obstacle-aware harnesses: route planning grounds objects as bounding boxes and draws candidate waypoint routes over them so the agent plans, verifies and replans before executing, while contact execution selects the contact position so the contact itself avoids the obstacle. The result is 71.9% task success and 87.5% collision avoidance, beating the previous state of the art by 6.5 and 27.0 points and reaching 2.3x and 1.5x the same agent without harnesses.

Agile-WAM: A Lightweight Tactile World Action Model for Contact-Rich Control

World Action Models (WAMs) go beyond conventional visuomotor policies by jointly predicting future world states and robot actions, letting the policy learn the physical dynamics that support control. Recent tactile WAMs, however, tend to lean on large-scale pretrained generative backbones to capture contact-rich dynamics, which limits inference efficiency and flexible deployment. Agile-WAM addresses this by encoding visual and tactile observations into a shared latent that serves as the source of a direct vision-tactile-to-action flow-matching process, jointly generating latent representations of action chunks and of future visual and tactile latents. The key observation is that vision and touch evolve at inherently different timescales: adjacent visual frames are highly similar, while tactile signals can change abruptly upon contact. Agile-WAM therefore uses multi-horizon multimodal prediction, supervising the visual latent at a larger temporal offset while predicting the tactile latent for the next frame. Across nine simulated and five real-world contact-rich manipulation tasks it outperforms the strongest baseline in success rate while maintaining low inference latency; in five real-world experiments it delivers a 29.4% relative gain in overall success rate at 11.9 ms inference latency.

Agile-WAM：把触觉世界模型做「轻」——不必生成未来视频，也能预判接触

世界动作模型（World Action Model, WAM）通过同时预测未来的世界状态与机器人动作，让策略学到支撑控制所需的物理动力学，因而超越了传统的视觉运动策略。但近期的触觉 WAM 往往依赖大规模预训练的生成式主干来刻画接触丰富的物理动力学，代价是推理效率低、部署不灵活。本文提出 Agile-WAM，一个用于接触丰富操作的高效触觉世界动作模型：它把视觉与触觉观测编码进一个共享隐空间，并以该隐空间为起点做直接的「视觉-触觉到动作」流匹配，从而联合生成动作块的隐表示与未来的视觉、触觉隐表示。其关键观察是视觉与触觉信号的时间尺度本就不同——相邻视觉帧高度相似，而触觉信号在接触发生时会突变——因此提出多时域多模态预测：以更大的时间偏移监督视觉隐表示，同时只预测下一帧的触觉隐表示以捕捉精细接触动力学。在九个仿真与五个真实世界接触丰富操作任务上，Agile-WAM 在成功率上优于最强基线，同时保持低推理延迟；在五个真实实验中整体成功率相对提升 29.4%，推理延迟为 11.9 毫秒。

GeoAAC：用去噪轨迹的几何形状，给 VLA 策略自适应决定动作块长度

动作分块（action chunking）已成为视觉-语言-动作（VLA）策略生成与执行动作的常规做法，但现有方法普遍使用固定的动作时域。同一次执行中，不同任务阶段对动作连续性、控制精度与闭环反馈的需求并不相同，固定时域无法适应这种变化。作者提出 GeoAAC：一种面向流匹配（Flow Matching）式 VLA 策略、基于几何的自适应动作分块方法，按当前动作预测的可靠程度调整动作时域。其核心观察是，流匹配去噪轨迹的几何形状提供了刻画预测可靠性的过程级信息——动作前缀之间的几何变化量与预测不确定性正相关。GeoAAC 用这种前缀级几何构造「时域级几何剖面」，在单次生成内自适应确定动作时域，且不需额外训练。在 GR00T N1.5 与 π0.5 上、于 LIBERO、LIBERO-Pro、RoboCasa365 及真实机器人操作任务上的实验显示，方法一致优于固定时域基线与已有自适应方法：仿真中最多提升 8.7 个百分点，真实世界平均成功率从 53.3% 提升到 74.4%。

SafeHarness：让「不要碰到障碍」真正进入代码智能体的规划与接触

代码智能体正在成为机器人操作的一条新路径：语言模型把控制器写成程序，由此构建的智能体无需机器人专门训练即可驱动机器人。但这条路径是否安全，此前无人追问。作者为每个操作任务配一个「绝不能触碰」的障碍物，发现智能体在大多数情况下仍然撞上障碍；它在推理轨迹中明确讨论了障碍，提示词也早已禁止触碰，因此问题既不在感知也不在指令，而在规划——这条约束从未成为优先事项。作者把操作拆成「路径阶段」与「接触时刻」两段来定位失效来源，并据此提出 SafeHarness：以障碍感知的路径规划（把物体落成包围盒，在其上把候选路径画成路点序列，先规划、再校验、必要时重规划、最后执行）与障碍感知的接触执行（选择接触位置，使接触本身避开障碍）两副外挂，让模型把安全约束排到前面。结果是 71.9% 的任务成功率与 87.5% 的避障率，分别比此前 SOTA 高 6.5 与 27.0 个百分点，也是同一智能体不加外挂时的 2.3 倍与 1.5 倍。

VLA-ULAP: Interleaving Cloud VLA Calls with Ultra-Lightweight Local Action Prediction at the Edge

Billion-parameter vision-language-action (VLA) policies demand substantial onboard power, while putting inference in the cloud runs into communication delay. VLA-ULAP interleaves remote VLA calls with an Ultra-Lightweight Local Action Predictor (ULAP). At roughly 7.4M parameters including a frozen vision encoder, ULAP fuses the current view, proprioception and executed action history in a single pass to predict action chunks, and trains independently: it needs no VLA hidden states, no online verification and no server round trips. On a Jetson Orin Nano, ULAP takes 19.9 ms and 0.183 J per inference, against 284.3 ms and 50.55 J for GR00T on an RTX A6000. Across three simulated base-policy/benchmark pairs, selected operating points remove 48.8-76.7% of VLA calls while retaining 95.0-97.5% of baseline success. On physical SO-101 experiments the system retains 95.2-100% of baseline success across seen and held-out placements, cutting estimated inference time by 47.9-58.0% and inference-device energy by 52.1-62.5%. In latency-aware LIBERO-Safety simulation it also exceeds pi-0.5 by 11.0 and 15.5 percentage points on two dynamic tasks.

PASSAGE: Scaling Scene-Aligned Motion Learning for Perceptive Humanoid Traversal in Cluttered Environments

PASSAGE is a perception-conditioned planner-tracker framework for humanoid traversal of cluttered environments. The authors collect 100 hours of scene-aligned human motion with virtual reality and inertial motion capture across 1,500 cluttered scenes; a conditional flow-matching planner generates short-horizon references from motion history, a local destination and a robot-centric multi-layer elevation map, and a perceptive whole-body tracker executes them at 50 Hz with geometric feedback. Real-time chunking promotes inter-chunk consistency, and planner-side reinforcement learning on top of the frozen tracker further improves closed-loop performance. The system needs no skill annotations and no obstacle-specific policies: one planner-tracker pair learns to select and compose stepping, squeezing and ducking on geometries it has never seen. Scaling captured data from 6 to 100 hours raises mean contact-free success on held-out scenes from 48.1% to 68.9%, and 70.3% with validated scene augmentation. The fully onboard system integrates egocentric 3D LiDAR, online occupancy mapping, 6.25 Hz planning and 50 Hz control on a Jetson AGX Orin, traversing 50 unseen physical layouts without prebuilt maps or offboard computation.

PointZero: 3D Point Track Completion for Learning Transferable 3D Dynamics

World models let perceptual systems predict how a scene will evolve under interaction, and they benefit most from large and diverse training volumes. Existing methods typically need robot action labels to learn action-conditioned 3D dynamics, which excludes web video from the training pool. This paper studies 3D point track completion as a pretraining objective that learns transferable 3D dynamics without any robot data: given a single RGB-D observation and sparse partial 3D trajectories, the model must predict the future 3D tracks of all observed points. The authors contribute a 2.9-million-frame synthetic dataset spanning deformable, articulated and rigid objects, and train PointZero, an expressive transformer, on it. Post-trained for action-conditioned 3D dynamics, PointZero beats baselines on the recent PGND benchmark; post-trained to predict robot actions and 3D tracks, it outperforms or matches baselines on 6 of 7 simulated and real-world manipulation tasks. Training from scratch isolates architecture gains from those of the objective and dataset, and the dataset, checkpoints and full training recipe are released.

VLA-ULAP：把云端 VLA 调用与端侧超轻量动作预测交替执行

十亿参数级的视觉—语言—动作（VLA）策略既需要可观的机载功耗，又受制于远程推理的通信延迟。VLA-ULAP 提出把远程 VLA 调用与一个超轻量本地动作预测器（ULAP）交替执行：ULAP 连同冻结的视觉编码器共约 740 万参数，一次性融合当前视图、本体感知与已执行动作历史来预测动作块，可独立训练，不需要 VLA 的隐状态、在线校验或服务端往返。在 Jetson Orin Nano 上，ULAP 单次推理为 19.9 毫秒与 0.183 焦耳，而 GR00T 在 RTX A6000 上是 284.3 毫秒与 50.55 焦耳。在三组仿真「基座策略—基准」组合上，所选工作点可去掉 48.8% 至 76.7% 的 VLA 调用，同时保留基线成功率的 95.0% 至 97.5%。在 SO-101 真机上，系统在已见与留出摆放上保留基线成功率的 95.2% 至 100%，推理时间估算降低 47.9% 至 58.0%，推理设备能耗降低 52.1% 至 62.5%；在感知延迟的 LIBERO-Safety 仿真中，其动态任务成功率还比 π0.5 高出 11.0 与 15.5 个百分点。

PASSAGE：用 100 小时场景对齐人体动作，训练能在杂乱环境中穿行的人形机器人

PASSAGE 是一个感知条件化的规划器—跟踪器框架，用于人形机器人在杂乱场景中的穿越。作者用虚拟现实与惯性动捕采集了 1500 个杂乱场景中 100 小时场景对齐的人体动作；条件流匹配规划器依据运动历史、局部目标点与机器人中心多层高程图生成短时域参考，具备感知能力的全身跟踪器再以 50 Hz 带几何反馈执行。实时分块保证块间一致性，规划器在冻结的跟踪器之上做强化学习后训练以提升闭环性能。整套系统不需要技能标注，也不用为每种障碍单独训练策略：同一对规划器—跟踪器即可在下台阶、侧身挤过、低头钻过之间自主选择与组合。把采集数据从 6 小时扩到 100 小时，held-out 场景的平均无接触成功率由 48.1% 提升到 68.9%，加入经校验的场景增强后达到 70.3%；全机载版本集成第一视角三维激光雷达、在线占据栅格建图、6.25 Hz 规划与 50 Hz 控制，在 Jetson AGX Orin 上于 50 个未见真实布局中完成穿越。

PointZero：把三维点轨迹补全当作预训练目标，学习可迁移的三维动力学

世界模型让感知系统能够预测场景在交互下如何演化，而这类模型最受益于大规模多样化数据。既有方法通常需要机器人动作标注才能学习「以动作为条件」的三维动力学，于是把网络视频排除在训练池之外。本文研究把三维点轨迹补全（3D point track completion）作为预训练目标，从而在没有机器人数据的情况下学习可迁移的三维动力学：给定一次 RGB-D 观测与稀疏的部分三维轨迹，模型需要预测所有已观测点未来的三维轨迹。作者贡献了一个覆盖可变形、铰接与刚性物体的 290 万帧合成数据集，并在其上训练了 Transformer 模型 PointZero。在下游的「以末端执行器位姿为条件的三维动力学预测」与「模仿学习」两项任务上，PointZero 在 PGND 三维动力学基准上优于基线，在 6/7 项仿真与真实机器人操作任务上优于或持平基线；作者还从零训练以分离架构、预训练目标与数据集各自的贡献，并公开数据集、检查点与完整训练配方。

TIO-Former: Ultra-Lightweight 6-Directional ToF-Inertial Odometry for Nano-UAVs

Autonomous nano-UAV navigation demands accurate ego-motion estimation under severe size, weight, power and computing constraints, yet cameras and LiDAR exceed payload limits, optical flow degrades in low-texture scenes, and inertial-only estimation drifts. Multi-zone time-of-flight arrays are a lightweight metric complement, but recovering 6-DoF motion from only 384 ranges per frame must contend with invalid returns, anisotropic observability and computation that scales with flight time. TIO-Former is a camera-free, optical-flow-free and mapless range-inertial odometry framework driven by an IMU and a 15 g payload of six orthogonal 8x8 ToF arrays. Its frontend pairs consecutive range grids with a bilateral gated difference, while IMU-guided cross-attention routes directional features conditioned on platform kinematics; a streaming causal transformer couples an uncompressed local KV cache with compressed Chunk-FIFO memory so inference cost and memory stay bounded regardless of flight duration. In real flights it cuts open-loop position error by 54.4% versus nano-UAV optical flow and by 66.4%-89.1% versus learned inertial baselines, running on an edge RISC-V companion at 10.466 ms P95 latency and 6.324 MiB peak resident memory. Code is released.

XPACE: Joint World and Action Modeling from Heterogeneous Experience

XPACE is a unified embodied world model that serves at once as a world action model, jointly predicting executable robot actions and future video, and as a world simulator that predicts the visual consequences of prescribed actions. A shared video backbone lets action-unlabeled video teach visual dynamics while action-labeled human and robot demonstrations jointly teach video and action prediction. A coarse-to-fine curriculum progressively shifts emphasis toward robot control while retaining human experience, so the policy acquires behaviors absent from robot demonstrations. The model then adapts its simulator to its own generated context, synthesizes deviation-recovery trajectories around expert demonstrations, and fine-tunes the policy on filtered recovery data. On XPENG's IRON humanoid, heterogeneous training improves robustness and transfers human-observed skills, and self-generated recovery data raises real-world task completion.

ModAR: Modality-Autoregressive World-Action Models

World-action models (WAMs) jointly model future observations and actions, and they almost always represent the predicted future as RGB images; yet depth maps, pretrained visual features and point tracks capture geometric, semantic and motion information far more efficiently. How best to combine these modalities inside a WAM has remained open. ModAR is the first WAM to autoregressively denoise multiple future modalities before predicting actions, so every prediction conditions on the modalities generated so far. Trained from scratch, it systematically studies training-data mixtures, which modalities to predict, and WAM formulations. The findings are that predicting point tracks, DINO features and depth maps helps, while additionally predicting future RGB gives no consistent benefit. ModAR's sequential generation attains the highest average success rate at every evaluated data scale, and when a video-model-initialized WAM, Flex-pi, is fine-tuned on the same data, ModAR reaches a slightly higher observed average success rate (75% vs 72%) using roughly 20x fewer training FLOPs and no pretraining. On three real-world bimanual tasks it beats baselines and improves further with human videos.

TIO-Former：为纳米无人机打造的极轻量六向 ToF-惯性里程计

自主纳米无人机需要在严苛的尺寸、重量、功耗与算力约束下完成精确自运动估计，而相机与激光雷达超重、光流在低纹理场景退化、纯惯性解算又会漂移。多区飞行时间（ToF）阵列提供了轻量的度量补充，但仅凭每帧 384 个测距值做 6 自由度估计，要面对无效读数、各向异性的可观测性与随时间增长的计算量。本文提出 TIO-Former：一个无相机、无光流、无地图的测距-惯性里程计框架，仅依赖 IMU 与六个正交 8x8 ToF 阵列组成的 15 克载荷。前端用双边门控差分对齐相邻距离网格，并以 IMU 引导的交叉注意力按平台运动学动态路由方向特征；流式因果 Transformer 把未压缩的本地 KV 缓存与压缩的分块 FIFO 记忆结合，使推理开销与内存占用不随飞行时长增长。真实飞行中，它把开环位置误差相比纳米无人机光流降低 54.4%，相比学习型惯性基线降低 66.4%-89.1%，并在 RISC-V 边缘计算单元上实现 10.466 毫秒 P95 延迟与 6.324 MiB 峰值常驻内存。代码已开源。

XPACE：把世界模型与动作模型合成一个模型，用异构经验训练人形机器人

本文提出 XPACE，一个把世界模型与世界-动作模型合二为一的统一具身世界模型：它既能联合预测可执行的机器人动作与未来视频，也能作为模拟器预测给定动作的视觉后果。作者用无动作标注的视频学习视觉动力学，用带动作标注的人类与机器人演示联合学习视频与动作预测，并采用由粗到细的课程逐步把训练重心转向机器人控制，从而使策略学到机器人演示未覆盖的行为。该模型还用自身模拟器合成偏离—恢复轨迹来补充监督，在 XPENG IRON 人形机器人上提升了鲁棒性、跨任务技能迁移与真实任务完成率。

ModAR：先自回归去噪多种未来模态，再预测动作的世界-动作模型

世界-动作模型（WAM）通常联合建模未来观测与动作，并把未来观测默认为 RGB 图像；但深度图、预训练视觉特征与点轨迹能更高效地承载几何、语义与运动信息，如何在 WAM 中组合这些模态仍是开放问题。本文提出 ModAR，第一个在预测动作之前先自回归地去噪多种未来模态的 WAM，使每一次预测都以上一步生成的模态为条件。作者从零开始训练，系统研究训练数据配比、预测模态与 WAM 形式的影响，发现预测点轨迹、DINO 特征与深度图能带来收益，而额外预测未来 RGB 并没有一致的增益。ModAR 的平均成功率在所有评测数据规模上都高于既有 WAM 形式；在与用视频模型初始化的 Flex-π 同数据微调对比时，它以约二十分之一的训练算力、不做预训练，取得略高的观测平均成功率（75% 对 72%）。在三个真实双臂任务上，ModAR 优于基线并能从人类视频中继续获益。

Size Doesn't Matter: Material-State Reinforcement Learning for Transferable Excavator Soil Manipulation

The paper casts autonomous earthmoving as material-state-conditioned reinforcement learning, training policies inside a GPU-parallel Material Point Method particle simulation so the controller can condition on soil shape and compactness, use multiple contact faces of the bucket, and displace material both into and out of the shovel. Policies are learned in a normalized end-effector space and deployed through a calibrated machine interface, so the same learned weights drive an 11.5 t hydraulic excavator and a 500 g tabletop robot. In the field the system autonomously built a 42 m long, 2.1 m high embankment, completing 201 consecutive policy strokes in 45 minutes with no failures, retries, or operator intervention, matching an expert operator's pace.

Pelican-Sim 1.0: A General World Model Simulator for Embodied Intelligence

Pelican-Sim 1.0 is a general world model simulator for embodied intelligence: it predicts future observations from visual context and robot actions to support downstream learning and decision making. Four design choices carry the system — a 28-dimensional action value space covering most mainstream embodiments so that one model stays valid across heterogeneous devices, action-visual injection that bridges actions and pixels with URDF- and camera-rendered action videos, sparse mixture-of-experts layers that absorb the action modality while reducing inter-modality conflict, and a four-step autoregressive simulator produced by causal adaptation and few-step distillation. Trained on roughly one million real-world and simulated trajectories, it improves video quality and action controllability substantially; in four downstream RoboTwin applications, adding 500 generated trajectories per task to 50 demonstrations raises policy success from 70% to 93%.

Dynin-Robotics: An Omnimodal Unified Diffusion Vision-Language-Action Model

The paper brings visual goal prediction and dynamics prediction into the generation and selection of actions by sharing a single trajectory model between them. It is built on Dynin-Omni, an omnimodal masked-diffusion backbone that represents language, visual observations, goals, and actions as discrete tokens. By varying only the conditioning and target spans, the same model learns action prediction, action-conditioned next-observation prediction, terminal goal-state prediction, and trajectory-to-instruction reconstruction. Those interfaces support test-time scaling through goal prediction, action-candidate evaluation, and joint refinement of action and future-state predictions. The authors continually pretrain on roughly 1.33 million trajectories from 48 Open X-Embodiment datasets and reach a 78.4% average success rate across four manipulation conditions on a Franka Research 3 robot.

Pelican-Sim 1.0：面向具身智能的通用世界模型仿真器

Pelican-Sim 1.0 是一个面向具身智能的通用世界模型仿真器：它根据视觉上下文与机器人动作预测未来观测，以支撑下游的学习与决策。模型包含四项关键设计——覆盖主流本体的 28 维统一动作空间、用 URDF 与相机渲染的动作视频打通动作与像素的动作-视觉注入、用于吸收异构动力学并缓解模态冲突的稀疏混合专家层，以及借助因果适配与少步蒸馏得到的四步自回归仿真器。作者在约一百万条真实与仿真轨迹上训练，并在多个数据集上取得视频质量与动作可控性的显著提升；在 RoboTwin 的四个下游应用中，每任务仅需补充 500 条生成轨迹即可把策略成功率从 70% 提升到 93%。

Size Doesn't Matter：材料状态强化学习让挖掘机学会可迁移的土壤作业

本文把土方作业的自主控制建模为材料状态条件化的强化学习问题，在 GPU 并行的材料点法（MPM）颗粒仿真中训练策略，使其能依据土壤形状与密实度等状态选用铲斗的不同接触面，把土体在铲斗内外双向搬运。策略在归一化末端执行器空间中学习，并通过标定后的机器接口跨机型部署：同一组权重既驱动 11.5 吨液压挖掘机，也驱动 500 克桌面机器人。实机测试中系统自主修筑了一条 42 米长、2.1 米高的土堤，45 分钟内连续完成 201 次策略动作，全程无需人工干预，推进速度与熟练操作员持平。

Dynin-Robotics：把动作、目标与动力学预测统一进一个全模态扩散 VLA

本文把视觉目标预测与动力学预测带进动作的生成与选择环节，方法是通过一个共享的轨迹模型来统一表达它们。模型构建在 Dynin-Omni 全模态掩码扩散骨干之上，将语言、视觉观测、目标与动作都表示为离散 token；只需改变条件区间与目标区间，同一个模型就能同时学会动作预测、动作条件下的下一观测预测、终末目标状态预测，以及轨迹到指令的重建。这些接口支持推理期扩展：通过目标预测、动作候选评估与动作-未来状态的联合细化来提升成功率。作者在约 133 万条、来自 48 个 Open X-Embodiment 数据集的轨迹上继续预训练，并在四个操作条件下于 Franka Research 3 机器人上取得 78.4% 的平均成功率。

ActSafeGuard: Training-Aligned Hard-Constraint Enforcement for Flow-Matching Robot Policies

Vision-language-action models and world-action models generate capable manipulation behavior, but their actions can violate hard physical constraints and therefore be unsafe or infeasible to deploy. Existing safety approaches either optimize statistical safety objectives without deterministic per-step guarantees, or correct unsafe actions only at inference time, creating a mismatch between policy training and execution. ActSafeGuard is a differentiable, training-aligned safeguard layer for flow-matching policies that integrates hard action feasibility into policy learning rather than treating safety as an inference-time add-on. An analytic ray-scaling operator yields boundary-aware gradients that guide the model to learn constrained manifolds. Across multiple standard foundation backbones (π0.5 and Fast-WAM) and a range of tasks, it consistently achieves a 100 percent step safety rate while fully preserving or even boosting task success.

In-Hand Pen Writing for an Anthropomorphic Hand, Learned in Real Time Without Demonstrations

The authors present an embodied control approach that estimates the task Jacobian of the combined hand-and-object system in real time on the physical robot. Using only a laptop CPU, the controller begins in-hand pen writing after roughly 18 seconds of initialization and keeps adapting online, with no analytic hand-object kinematic or contact model, no simulation training, and no precollected demonstrations. The same estimator/controller formulation works on three anthropomorphic hands (one physical, two simulated), demonstrating an embodiment-independent method. On the physical robot it reaches sub-millimeter in-plane precision (mean 0.6 mm across runs) for letters and shapes written both in the air and on paper, which the authors describe as the first demonstration of an anthropomorphic hand writing arbitrary single-stroke trajectories with a grasped pen through purely in-hand motion.

EVPeriscope: Event-Camera Propeller Tracking Gives Air and Ground Robots Mutual Perception

EVPeriscope equips a ground robot with an upward-facing event camera that picks up the high-frequency visual signature of a quadrotor's propellers, enabling detection, localization, and closed-loop control of the aerial vehicle without fiducial markers and without the motion-blur and lighting sensitivity of frame-based cameras. The quadrotor can therefore act as an extended perception system for the ground robot when its own sensors degrade or are occluded, forming a marsupial air-ground team. The authors run field experiments in winds up to 15 mph, in daylight and at night, and demonstrate localization and closed-loop navigation through dense foliage where the ground robot's sensors are blocked; quadrotor control runs at 200 Hz entirely on onboard sensing and computation.

ActSafeGuard：把硬约束「训进」流匹配策略，实现 100% 单步安全

视觉-语言-动作（VLA）与世界-动作模型（WAM）在通用机器人操作上表现出色，但其生成的动作可能违反硬性物理约束，从而在部署中不安全或不可行。现有安全方法要么只优化统计意义上的安全目标、缺乏逐步骤的确定性保证，要么只在推理时纠正不安全动作，造成策略训练与执行之间的错位。ActSafeGuard 是一个面向流匹配（flow matching）策略的可微、且与训练对齐的安全防护层，把硬性动作可行性融进策略学习本身，而非把安全当作推理期的外挂。作者设计的解析射线缩放（ray-scaling）算子使梯度具有边界感知能力，引导模型自然地学到受约束的动作流形。在多个标准基础骨干（π0.5 与 Fast-WAM）与多种任务上的实验显示，ActSafeGuard 持续取得 100% 的单步安全率，同时完整保持甚至提升任务成功率。

拟人机械手实现手内握笔书写：实时雅可比估计，无需仿真训练与示教数据

本文提出一种具身控制方法，在真实机器人上实时估计「手＋被握持物体」组合系统的任务雅可比。控制器只用一台笔记本的 CPU，在约 18 秒初始化后即开始手内握笔书写，并在运行中持续在线自适应；整个过程不需要手-物体的解析运动学或接触模型、不需要仿真训练，也不需要预先采集的任务演示。同一套估计器/控制器在三套拟人机械手（一台实物、两套仿真）上均成立，说明该表述与具体本体无关。在实物机器人上，无论在空中还是在纸面书写字母与图形，都能达到亚毫米级的面内精度（多轮运行均值 0.6 mm）；作者称这是首次演示拟人机械手仅靠纯手内运动、用握持的笔写出任意单笔画轨迹。

EVPeriscope：地面机器人抬头，用事件相机追踪螺旋桨与无人机互为空地感知延伸

EVPeriscope 让地面机器人用一台朝上的事件相机捕捉四旋翼螺旋桨的高频视觉特征，从而在不需要基准标记、也不受运动模糊与光照变化拖累的前提下完成对无人机的检测、定位与闭环控制。无人机由此可以在自身传感器退化或被遮挡时充当地面机器人的「延伸感知」，形成地空「袋鼠式」协同系统。作者在上至 15 mph 风速的野外条件下、白天与夜间都做了实验，并演示了在地面机器人传感器被茂密植被遮挡时依靠该系统完成定位与闭环导航；对无人机的控制以 200 Hz 运行，完全依赖机载传感与计算。

GTA-2: A Multi-VLM Framework for Synthesizing Robot Manipulation Skills via Grounded Task Axes

GTA-2 is a modular multi-VLM framework that constructs executable, task-bespoke manipulation skills from reusable object-centric task-axis components rather than predicting actions end-to-end or composing fixed primitives. Each skill is represented as semantic subtasks comprising task-relevant keypoints and axes, controller compositions, and scene-dependent parameters. Four specialized VLM agents separately decompose the task, construct an abstract task-axis skill, assign controller parameters, and ground required visual features from RGB-D observations, enabling zero-shot skill generation with no task-specific demonstrations, policy training, or fine-tuning. On 14 real-robot manipulation tasks it achieves 73.9% average zero-shot success, exceeding the strongest baseline by 31.4 percentage points, and targeted human feedback refines an incorrect stage to raise the average to 90.7%.

FolDeX: A Physical-World Benchmark for Long-Horizon Robotic Manipulation of Deformable Objects

FolDeX is a physical-world benchmark built entirely from real-robot data, centered on garment folding, that targets long-horizon deformable-object manipulation where policies must track changing states and execute reliable multi-stage bimanual interactions. Because real-robot data is costly, it studies efficient reuse of heterogeneous physical experience along four axes: human intervention and recovery data collected during deployment; transfer across tasks including garment categories and rigid-to-deformable manipulation; reuse across scenes with changing lighting, background, and layout; and transfer across embodiments. The benchmark provides 2,000+ hours of real-robot data spanning 20+ tasks and 10+ embodiments, plus a fair evaluation platform for externally submitted policies with standardized tasks, held-out physical objects, controlled initializations, and a unified execution protocol.

Learning Terrain-Adaptive Humanoid Locomotion on Granular Terrain

This work builds a physics-grounded granular contact model from three-dimensional resistive force theory (3D RFT) and simulates granular terrain efficiently for reinforcement learning, replacing rigid or heuristic contact solvers with physically accurate intrusion dynamics that capture penetration and tangential drag. A terrain-adaptive controller is trained via teacher-student RL, with a variational autoencoder compressing terrain information into a compact latent for zero-shot terrain identification and adaptation. MPM/NVIDIA Newton studies show generalization to unseen granular terrains and higher success rates than baselines, and hardware experiments on basalt, dry sand, and beach sand deliver what the authors state is the first demonstration of agile humanoid locomotion on real-world granular terrain.

A Traffic Management System for Large and Heterogeneous Vehicles in Narrow Industrial Environments

Coordinating automated guided vehicles (AGVs) in high-density industrial environments is a critical challenge within Logistics 4.0, where traditional negotiation-based priority assignment in traffic management often causes inefficiency. This paper presents an AGV traffic management system based on lifelong multi-agent path finding (L-MAPF) operating on roadmaps generated with non-uniform rational B-spline (NURBS) curves, guaranteeing locally optimal coordination and safe operation of large, heterogeneous AGVs. The framework integrates a modified bounded-horizon conflict-based search (CBS) within a rolling-horizon conflict resolution strategy, using an extended time horizon per agent to resolve conflicts effectively in corridors identified by a topological map. Unlike state-of-the-art AGV fleet traffic management, the solution targets real-world, non-standardized (non-grid-like) industrial settings with narrow bidirectional corridors and high traffic density, where AGVs of various sizes and capabilities operate simultaneously. Key contributions include an anytime conflict resolution strategy with adaptive time-horizon regulation, an execution layer for safe and standard-compliant interaction with real AGVs, and an advanced deadlock detection and resolution mechanism. Experiments in realistic industrial environments show throughput improvements of up to 11% over a conventional rule-based traffic management system, a state-of-the-art industrial method, and a priority-based L-MAPF variant, while maintaining continuous operation.

Frequency-Conditioned Flow Matching for Vision-Language-Action Models

Robot actions are temporally correlated trajectories whose frequency components encode motion at different scales with highly non-uniform energy distributions, yet flow-matching vision-language-action models typically generate actions in temporal coordinates without explicitly modeling or systematically exploiting this frequency heterogeneity. FreqFM raises action frequency from an implicit trajectory property to an explicit conditioning dimension that spans the entire generation pipeline. Concretely, in DCT frequency coordinates it constructs a spectrum-matched source distribution, adaptively balances the objective across frequencies, and constrains per-frequency guidance residuals using the corresponding reference transport scales. FreqFM integrates into existing flow-matching action experts without changing the VLA backbone, improving performance consistently across LIBERO, LIBERO-Plus, and VLA-Arena, including a 9.3-point gain on LIBERO-Plus, and further demonstrating effectiveness on six real-robot tasks.

Show-Harness: Just a VLM Agent Can Play Robots

Show-Harness is an embodied harness that converts the world knowledge of foundation vision-language models into robot control through a compact semantic interface. The VLM reasons over discrete semantic action units, and embodiment-specific interpreters deterministically ground those decisions into local robot actions, keeping the VLM directly responsible for fine-grained physical decisions. The same interface directly unlocks closed-source frontier VLMs for zero-shot robot control and adapts small-scale open-source VLMs for low-cost deployment with only a few GPU-hours of fine-tuning. Its companion GUMI (GUI Manipulation Interface) extends the same semantic action space to GUI-based demonstration collection, so humans and agents can play robots across embodiments without specialized teleoperation hardware. Extensive experiments show that Show-Harness-equipped VLM agents generalize robustly across tasks, embodiments, and environments, outperforming representative agentic and VLA paradigms.

GTA-2：基于具身任务轴的多VLM机器人操作技能合成框架

GTA-2是一个模块化多VLM框架，从可复用的、以物体为中心的任务轴组件出发，构建可执行、为具体任务量身定制的操作技能，而非端到端预测动作或组合固定原语。每项技能被表示为语义子任务，包含与任务相关的关键点与轴、控制器组合以及场景相关参数。四个专门化的VLM智能体分别负责：分解任务、构建抽象任务轴技能、分配控制器参数、并从RGB-D观测中落地所需视觉特征，从而实现零样本技能生成——无需任务专用示教、策略训练或微调。在14项真机操作任务上，平均零样本成功率达73.9%，比最强基线高出31.4个百分点；针对性的阶段级人工反馈可将平均成功率提升至90.7%。

FolDeX：面向可形变物体长时程机器人操作的真实世界基准

FolDeX是一个完全基于真机数据构建的真实世界基准，以衣物折叠为核心任务，面向长时程可形变物体操作——策略须跟踪不断变化的状态并执行可靠的多阶段双臂交互。鉴于真机数据采集成本高昂，该基准围绕四条研究轴考察异构物理经验的高效复用：利用部署中采集的人类干预与恢复数据；跨任务迁移（含衣物类别之间及刚体到可形变物体）；跨场景复用（光照、背景与布局变化）；以及跨本体迁移。基准提供2000+小时真机数据，覆盖20+任务与10+本体，并给出对外部提交策略的公平评测平台：标准化任务、留出实物、受控初始化与统一执行协议。

面向颗粒地形的自适应人形机器人运动学习

本文基于三维阻力理论（3D RFT）构建了物理上可靠的颗粒接触模型，并高效仿真颗粒地形用于强化学习训练，以能刻画下陷与切向拖曳的物理精确侵入动力学，取代刚体接触模型与启发式颗粒模型。控制器通过教师—学生强化学习训练，并用变分自编码器把地形信息压缩为紧凑隐变量，实现零样本地形识别与自适应。基于MPM与NVIDIA Newton的仿真研究表明方法可泛化到未见颗粒地形、成功率显著高于基线；在玄武岩、干沙与海滩沙等多种真实颗粒地形上的硬件实验，作者称是首次实现真实颗粒地形上的敏捷人形机器人运动。

面向狭窄工业环境的大型异构车辆交通管理系统

高密度工业环境中的自动导引车（AGV）协同是物流 4.0 的核心难题，传统基于协商的优先级分配式交通管理常因协商开销导致效率损失。本文提出一种基于终身多智能体路径规划（L-MAPF）的 AGV 交通管理系统，运行在由非均匀有理 B 样条（NURBS）曲线生成的路网上，保证局部最优协同并确保大型异构 AGV 的安全运行。框架把改进的有界时域冲突搜索（CBS）嵌入滚动时域冲突消解策略：为每个智能体设置更长的时域，以便在拓扑地图标定的走廊中有效消解冲突。与现有 AGV 车队交通管理方法不同，该方案面向真实、非标准化（非网格状）的工业场景——狭窄双向走廊、高交通密度、多种尺寸与能力的 AGV 同时运行。关键贡献包括带自适应时域调节的 anytime 冲突消解、面向真实 AGV 的安全合规执行层，以及死锁检测与消解机制。在真实工业环境中的实验显示吞吐量最高提升 11%，优于常规规则式交通管理与基于优先级的 L-MAPF 变体，同时保持连续运行。

面向视觉-语言-动作模型的频率条件流匹配

机器人动作是时间上相关的轨迹，其频率分量在不同尺度上编码运动，能量分布高度不均；而基于流匹配的视觉-语言-动作（VLA）模型通常在时间坐标中生成动作，并未显式建模或系统利用这种频率异质性。本文提出 FreqFM，一种面向 VLA 的频率条件流匹配框架：它把动作频率从轨迹的隐式属性提升为贯穿整个生成流程的显式条件维度。具体而言，在 DCT 频率坐标下，FreqFM 构造谱匹配的源分布、自适应地在各频率之间平衡训练目标，并用对应的参考传输尺度约束逐频率的引导残差。FreqFM 可无缝接入现有流匹配动作专家而不改动 VLA 主干，在 LIBERO、LIBERO-Plus 与 VLA-Arena 上一致提升，其中 LIBERO-Plus 提升 9.3 个百分点，并在六个真实机器人任务上验证有效。

Show-Harness：一个 VLM Agent 就能「玩」机器人

Show-Harness 提出一种「具身载体」接口，把基础视觉语言模型（VLM）的世界知识转化为机器人控制能力：它向 VLM 暴露一组离散的语义动作单元供其自然推理，再由本体相关的解释器确定性地把这些单元落地为局部机器人动作，使 VLM 直接对细粒度物理决策负责。同一接口既能零样本解锁闭源前沿 VLM 的机器人控制，也能用数 GPU 小时微调小规模开源 VLM 以实现低成本部署；配套的 GUMI（图形界面操作接口）把同一语义动作空间扩展到基于 GUI 的示教采集，让人与智能体无需专用遥操作硬件即可跨本体「玩」机器人。大量实验显示，搭载 Show-Harness 的 VLM 智能体在任务、本体与环境间稳健泛化，优于代表性 agentic 与 VLA 范式。

Ostrich: Taking Large Strides Through Stiff Contact in Differentiable Dynamics

Ostrich is a GPU-accelerated differentiable rigid-body simulator that resolves hard contacts and friction with non-smooth Newton iteration at large timesteps (~0.1 s) and differentiates the converged residual via the implicit function theorem, reusing the forward Schur complement to compute the adjoint at O(1) memory per timestep. On real-robot trajectories over a pallet obstacle, Ostrich holds MuJoCo's sim-to-real accuracy at up to 50x larger timesteps. Its gradients converge from random initializations where MJX descends slowly and Newton Semi-Implicit stalls; a warm iteration runs 211x faster than MJX and 4.7x faster than Semi-Implicit. Ostrich differentiates 8,192 parallel worlds on a single 24 GB GPU, sustaining 29x checkpointed MJX's optimization throughput. It closes with gradient-based trajectory optimization over triangle-mesh terrain across a 10 s horizon.

Proxy Policy Steering

Proxy Policy Steering (PPS) adapts a frozen generalist robot policy to a new task at inference time. Instead of modifying the base policy, PPS trains two lightweight proxy policies — a reference proxy that models the frozen base's behavior on target-task observations and a task proxy capturing how behavior changes under task supervision — and uses their calibrated velocity-space difference to steer the base sampler at every denoising step. Because the base is never modified, its broad capabilities remain intact. On 8 real-world and 4 simulation manipulation tasks, PPS lifts the state-of-the-art pi0.5 base policy by 53% absolute success rate on average, including zero-to-one gains on tasks the base never solved, outperforming LoRA fine-tuning, from-scratch specialists, residual policies, and prior inference-time steering methods.

TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model

TANGO is the first whole-body vision-language navigation framework for humanoid robots in cluttered indoor environments. Unlike 2D path-planning approaches, it treats traversal as a continuous geometry-aware whole-body problem: given a natural-language instruction and egocentric RGB observations, TANGO directly predicts 29-DoF joint-space actions. It is trained entirely in simulation using synthesized collision-free traversal behaviors built from global path planning, kinematic whole-body motion generation, obstacle-aware motion editing, and RL-based tracking. TANGO achieves state-of-the-art vision-language navigation results in simulation, outperforming strong modular baselines in obstacle-negotiation scenes, and deploys zero-shot on a Unitree G1 humanoid in cluttered real-world scenes without any real-world navigation training data.

Ostrich：在可微动力学中以大步长穿越刚硬接触

Ostrich是一个GPU加速的可微刚体仿真器：它以大步长（约0.1秒）用非光滑牛顿迭代求解硬接触与摩擦，并借助隐函数定理对收敛残差求导，复用前向Schur补以每时间步O(1)内存计算伴随梯度。在跨越托盘障碍的真实机器人轨迹上，Ostrich以高达50倍于MuJoCo的时间步长保持与其相当的仿真到现实精度。其梯度能从随机初始化收敛，而MJX下降缓慢、Newton Semi-Implicit直接停滞；一次热启动迭代比MJX快211倍、比Semi-Implicit快4.7倍。Ostrich可在单张24 GB GPU上并行求导8192个世界，达到带检查点MJX优化吞吐的29倍。论文最后展示了在三角网格地形上、10秒时域内的基于梯度的轨迹优化。

代理策略引导：面向冻结通用策略的推理时任务适配

Proxy Policy Steering（PPS，代理策略引导）是一种推理时适配方法，让冻结的通用机器人策略无需修改即可适应新任务。它训练两个轻量代理策略——一个「参考代理」建模冻结基座策略在目标任务观测上的行为，一个「任务代理」刻画任务监督带来的行为变化——用二者经校准的速度空间差值在每个去噪步引导基座采样器。由于基座策略从未被修改，其广泛能力得以完整保留。在8个真实世界与4个仿真操作任务上，PPS将当前最强的pi0.5基座策略的平均绝对成功率提升53%，在基座从未解决的任务上实现从零到一的突破，并全面优于LoRA微调、从零训练的专用策略、残差策略及此前的推理时引导方法。

TANGO：基于全身视觉-语言-动作模型的杂乱环境人形导航

TANGO是首个面向杂乱室内环境下人形机器人的全身视觉-语言导航框架。与二维路径规划不同，它把穿行视为连续的、几何感知的全身问题：给定自然语言指令与第一视角RGB观测，TANGO直接预测29自由度关节空间动作。模型完全在仿真中训练，通过全局路径规划、运动学全身动作生成、障碍感知动作编辑与基于强化学习的跟踪，合成多样的无碰撞穿行行为。TANGO在仿真视觉-语言导航中取得当前最优性能，在需要障碍协商的挑战性场景中优于强模块化基线，并在未使用任何真实导航数据的情况下，零样本部署到Unitree G1人形机器人上，在杂乱真实场景中实现稳健的语言引导穿行。

Collision Snapshot Guided Time-Reversed Safety-Critical Scenario Generation

COSTER is a framework for generating safety-critical traffic scenarios by leveraging learned traffic priors to determine plausible collision times and locations. It constructs a collision snapshot by inserting a new vehicle in contact with the target vehicle at an identified collision state, then uses a conditional variational autoencoder to perform a time-reversed rollout that reconstructs the inserted vehicle's trajectory backward toward earlier timesteps. Experiments show COSTER outperforms existing methods in plausibility, diversity, and data efficiency; agents trained on COSTER-generated scenarios reduce collision rates by 31% on safety-critical scenarios from the Waymo Open Motion Dataset while also improving ego task completion.

碰撞快照引导的时间反演安全关键场景生成

COSTER是一种利用学习到的交通先验来确定合理碰撞时间与位置的安全关键交通场景生成框架。它先在识别出的碰撞状态下插入一辆与目标车辆接触的新车，构造"碰撞快照"，再由条件变分自编码器执行时间反演展开，把插入车辆的轨迹向更早时刻逆向重建。实验表明COSTER在合理性、多样性与数据效率上均优于现有方法；在COSTER生成场景上训练的智能体，于Waymo Open Motion Dataset的安全关键场景中碰撞率降低31%，同时提升了主车任务完成度。

GLoRI: Closed-Loop Whole-Body Tracking with Global-Local Reference Interaction for Humanoid Loco-Manipulation

GLoRI is a closed-loop whole-body controller for humanoid loco-manipulation that integrates structured global reference and feedback with local motion guidance. Its GLoRI-Net uses Global-Local Cross Attention (GLCA) to refine local keypoint features with global target and pose-difference features, preserving motion structure while correcting world-frame placement. GLoRI achieves 100% completion and a g-MPJPE of 6.44 cm on held-out HuMoTo motions, and this accuracy remains robust under direct Isaac Gym-to-MuJoCo transfer without fine-tuning. The accuracy and generalization enable autonomous loco-manipulation with a single policy on a real Unitree G1 interacting with diverse unseen objects.

GloVLA: Let Geometry Move and Local VLA Interact for Robust Object-Centric Manipulation in Unstructured Environments

GloVLA is a hybrid framework that separates object-centric manipulation into two complementary regimes: a geometric transport controller moves the end-effector into interaction-centric handoff regions, while local Vision-Language-Action (VLA) policies handle only the short-horizon, contact-rich interaction phases. It is model-agnostic, integrates with different VLA backbones without additional demonstrations, and requires no changes to the action space or success predicate. Under clutter, distractors, illumination changes, visual shifts, and obstruction, full-trajectory GR00T N1.6 execution degrades to 20.9% average success while GloVLA retains 88.5%. On a physical UR10e, overall success improves from 35.6% to 90.0% while mean inference time is more than halved.

GLoRI：全局-局部参考交互的闭环全身跟踪——人形机器人移动操作

GLoRI是一种面向人形机器人移动操作的闭环全身控制器，将结构化全局参考与反馈同局部运动引导相结合。其GLoRI-Net采用全局-局部交叉注意力（GLCA），用全局目标与位姿差特征精化局部关键点特征，在保持动作结构的同时纠正世界坐标系下的空间位置。GLoRI在保留的HuMoTo动作上达到100%完成率、全局MPJPE 6.44厘米，且该精度在Isaac Gym到MuJoCo的直接迁移中无需微调依然稳健。凭借这一精度与泛化能力，GLoRI可在真实Unitree G1上以单一策略自主完成与多种未见物体的移动操作交互。

GloVLA：让几何负责移动、局部VLA负责交互——非结构化环境下的稳健以物为中心操作

GloVLA是一种混合式框架，将以物为中心的操作显式拆分为两个互补阶段：几何传输控制器负责把末端执行器移动到交互为中心的交接区域，局部视觉-语言-动作（VLA）策略只处理短时程、重接触的交互阶段。该方法与模型无关，可无需额外演示地集成不同VLA骨干，且不改变动作空间或成功判据。在杂乱、干扰物、光照变化、视觉偏移与遮挡等挑战条件下，全轨迹GR00T N1.6的平均成功率降至20.9%，而GloVLA仍保持88.5%；在真实UR10e机械臂上，整体成功率从35.6%提升到90.0%，平均推理时间缩短一半以上。

TacPAC: Tactile Prediction and Real-Time Action Correction in World-Action Models for Contact-Rich Manipulation

TacPAC turns tactile prediction into real-time action correction inside world-action models. Vision-centric predictions miss the local contact cues that decide contact-rich manipulation, yet naively predicting future tactile observations as extra views recovers only about a third of the achievable gain in the authors' experiments — a timing mismatch, since predictions precede execution while tactile feedback arrives during it. TacPAC caches the predicted contact a planned action chunk was conditioned on, together with the plan's own representation, and a tactile expert reads each newly observed tactile image against that cache to correct only the not-yet-executed actions. Feedback is interpreted against what the plan anticipated rather than in isolation, and a single correction pass costs 20.7x less than regenerating the chunk. On five real-robot tasks spanning precision insertion, fragile-object handling, object reorientation, and long-horizon manipulation, TacPAC leads every task and lifts average success from 22% for its vision-only base model to 64%. Code is open-sourced.

RoboSPA: Can VLA Models Go Beyond Simple Scenes and Short-Horizon Tasks?

RoboSPA (Robot Spatial-Procedural Assessment) is a large-scale robotic manipulation dataset and diagnostic benchmark for embodied reasoning in VLA models. It focuses on two core dimensions — fine-grained spatial reasoning and long-horizon procedural planning — covering 10 task categories and 56 base tasks, each instantiated across five difficulty levels to yield 280 variants with increasing spatial ambiguity and procedural complexity. The dataset contains 527K trajectories across multiple embodiments and diverse scenes. Beyond binary success rate, RoboSPA introduces diagnostic metrics for finer-grained evaluation. Experiments on representative VLA models show current systems still struggle with complex spatial relations, precise low-level execution, and memory-intensive planning, establishing RoboSPA as a challenging diagnostic benchmark for more capable, reliable, and generalizable embodied agents. Data and code are public.

FIRE-LIVWO: Robust LiDAR-Inertial-Visual-Wheel Odometry via Failure-Immune mmWave Radar Enhancement

FIRE-LIVWO is a failure-immune multi-modal odometry framework for large-scale underground coal mines. Built on an iterated error-state Kalman filter (IESKF), it tightly couples 4D mmWave radar, LiDAR, visual features, and wheel odometry inside a unified VoxelMap, jointly constructing LiDAR-radar point-to-plane residuals and sparse visual photometric residuals. In smoke-filled environments it exploits mmWave radar penetration with pointwise Doppler velocity constraints to preserve observability; in geometrically degenerate corridors it couples wheel odometry via non-holonomic constraints with online lever-arm compensation. Its central contribution is a degeneration-detection and adaptive fusion-model switching strategy grounded in geometric and visual observability analysis that quantifies observability online and dynamically adjusts modality weights. Real-world underground coal-mine experiments show reliable failure-boundary identification and modality switching with a 5.677 m average localization error, outperforming baselines; code is open-sourced.

What Matters, When? Diagnosing and Improving Conditional Visual Grounding in Visuomotor Imitation Policies

This paper diagnoses why visuomotor imitation policies fail when visually similar distractors appear, framing the problem as conditional visual grounding: the visual target needed for control changes with the manipulation phase and, in complex tasks, with the observed task state. Using Action Chunking with Transformers (ACT), the authors systematically introduce distractors with controlled color and shape similarity and localize failures to picking and placement stages, finding sensitivity depends on both similarity type and stage. Guided by the diagnosis, they combine distractor augmentation, phase-dependent attention regularization, and appearance-based visual prompting, substantially improving robustness in simulation and on a physical UR3e, and show the same failure pattern in a pretrained vision-language-action policy on a state-conditioned instrument-handling task.

Same Trajectory, Contradictory Rewards (ROBORMBENCH): Paraphrase Fragility in Vision Language Reward Models

ROBORMBENCH measures a neglected failure mode in VLM-based reward models for robot learning: paraphrase fragility. Using 2,390 real-robot trajectories with ground-truth progress labels and 21,673 verified paraphrases (lexical, syntactic, and action-goal rewrites), the authors show that merely rewording an instruction can substantially change predicted reward and even flip identical behavior between success and failure. The instability is widespread across proprietary and open-source VLMs, grows with more divergent rewrites, and is not reliably fixed by model scale or explicit reasoning; dedicated reward models trained with trajectory-grounded supervision are markedly more stable. The work establishes paraphrase robustness as a core requirement for reliable VLM reward modeling.

TacPAC：面向接触丰富操作的世界动作模型触觉预测与实时动作校正

TacPAC把触觉预测转化为世界动作模型中的实时动作校正。纯视觉世界模型对未来的预测缺少决定接触丰富操作成败的局部接触线索，而朴素地把未来触觉观测当作额外视角预测，在实验中只能收回约三分之一的可实现增益——根源在于时间错配：预测先于执行，触觉反馈却在执行中才到达。TacPAC在基础模型规划出动作块后，缓存该规划所依据的预测接触及其表征，由触觉专家模型将每个新触觉图像与该缓存比对，仅校正尚未执行的动作，单次遍历缓存即完成校正，比重新生成动作块便宜20.7倍。在精密插入、易碎物操作、物体重定向与长时程操作五类真实机器人任务上全面领先，平均成功率由纯视觉基础模型的22%提升至64%，代码已开源。

RoboSPA：VLA模型能否超越简单场景与短视距任务？

RoboSPA（机器人空间-程序化评估）是一个大规模机器人操作数据集与诊断基准，用于检验VLA模型在空间与程序复杂度递增条件下的具身推理能力。基准聚焦两个核心维度：细粒度空间推理与长时程程序化规划，覆盖10个任务类别、56个基础任务，每个任务在五个难度级别上实例化，共280个变体，空间歧义与程序复杂度逐级递增。数据规模为527K条轨迹，覆盖多种本体与多样场景。除二值成功率外，RoboSPA引入诊断性指标进行更细致的评估。对代表性VLA模型的实验显示，现有系统在复杂空间关系、精确低层执行与记忆密集型规划上仍力不从心，确立了RoboSPA作为开发更强、更可靠、更泛化具身智能体的挑战性诊断基准，数据与代码已公开。

FIRE-LIVWO：毫米波雷达增强的煤矿井下多模态鲁棒里程计

FIRE-LIVWO是一种面向大型地下煤矿的故障免疫多模态里程计框架：在迭代误差状态卡尔曼滤波（IESKF）中紧耦合4D毫米波雷达、LiDAR、视觉与轮式里程计，于统一VoxelMap中构建LiDAR-雷达点到面残差与稀疏视觉光度残差。烟尘环境下利用毫米波雷达强穿透并引入逐点多普勒速度约束维持可观性，退化巷道中通过非完整约束与在线杆臂补偿抑制漂移；核心贡献是基于几何与视觉可观性分析的退化检测与自适应融合模型切换策略，在线量化可观性并动态调整模态权重。真实煤矿井下实验表明其能准确识别失效边界并可靠切换模态，平均定位误差5.677米，优于对比基线，代码已开源。

什么在何时起作用？视觉运动模仿策略中条件视觉定位的诊断与改进

本文诊断了视觉运动模仿策略在出现视觉相似干扰物时失效的原因，将问题归结为条件视觉定位：控制所需的视觉目标会随操作阶段变化，在复杂任务中还会随观测到的任务状态变化。作者以 ACT（Action Chunking with Transformers）为对象，系统引入颜色与形状相似度受控的干扰物与容器，将失败定位到抓取与放置阶段，发现敏感性同时取决于相似类型与操作阶段。基于这一诊断，他们组合了干扰物增强、阶段相关注意力正则化与外观视觉提示三种手段，在仿真与实体 UR3e 上显著提升鲁棒性，并在预训练 VLA 的状态条件器械操作任务中复现了同样的失效模式。

同一轨迹，互相矛盾的奖励：ROBORMBENCH 揭示视觉语言奖励模型的改写脆弱性

ROBORMBENCH 系统度量了 VLM 奖励模型的一个被忽视的失效模式——改写脆弱性。基于 2,390 条带真实进度标签的真实机器人轨迹和 21,673 条经人工验证的改写指令（涵盖词汇、句法与动作目标层面），研究发现：仅仅换一种说法描述目标，就可能显著改变预测的奖励分数，甚至把完全相同的机器人行为从成功判成失败。这种不稳定在闭源与开源 VLM 中普遍且严重，随改写偏离程度加剧，且无法靠增大模型规模或显式推理可靠消除；而用轨迹监督训练的专用奖励模型则稳定得多。工作将改写鲁棒性确立为可靠 VLM 奖励建模的核心要求。

BRIDGE: An Open-Source Humanoid Platform via Morphology-Control Co-Design for Physical AI

BRIDGE is an open-source 88 cm humanoid platform produced by a data-driven morphology-control co-design framework that optimizes humanoid morphology for human-like movement. The framework introduces a metric jointly measuring kinematic retargeting fidelity to human motion and dynamic tracking performance, and the resulting platform outperforms baseline humanoids (Bumi, K1, Toddlerbot) across all reported metrics. The robot is released alongside its whole-body control policy, demonstrating foundational locomotion, robust balance, and highly dynamic maneuvers while capturing human motion data with superior fidelity.

Scaling Bimanual Household Manipulation from 1,500 Hours of Demonstrations to On-Policy Corrections

This work releases 1,500 hours of diverse bimanual household manipulation demonstrations and trains XR-2, a vision-language-action model, on the corpus through a purpose-built high-throughput data pipeline and a multi-stage training paradigm. Systematic experiments show strong task performance with favorable training efficiency and data utilization, and along two scaling axes — demonstration volume and post-training on DAgger correction data from real-time human interventions — task success improves steadily, exhibiting a clear scaling trend at current data scale. The dataset is open-sourced to support reproducible bimanual manipulation research.

MINERVA: How Small Can a Manipulation Policy Be and Still Solve LIBERO?

MINERVA measures the task-specific capacity floor of the LIBERO benchmark with deliberately compact visuomotor policies: a 0.54M-parameter policy reaches 95.1% average success over 2,000 rollouts across the four standard LIBERO suites — only 2.4 points below the reported LeRobot pi0.5 result with 7,700x fewer parameters. Performance saturates near 1M parameters and collapses below 0.25M. A task-ID permutation probe shows standard LIBERO instruction conditioning primarily selects among memorized tasks, and the tiny policy replans every control step in 5-9 ms on a laptop CPU — 113x faster than SmolVLA and 1,400x faster than pi0.5.

LaPla: Continuous Actions from Discrete Minds — Latent-Aligned Planning for End-to-End Autonomous Driving

LaPla is a unified Vision-Language-Action (VLA) framework with latent-aligned planning that grounds semantic understanding in precise motion execution. It builds an action tokenizer from a residual vector-quantized VAE that captures vehicle kinematics, then repurposes that representation as a continuous physical prior rather than a discrete codebook: concurrent action queries attend to multimodal context in a single forward pass and project hidden states directly into the pretrained latent space, where a frozen decoder translates them into actions — eliminating quantization error and bypassing autoregressive generation. On nuScenes, LaPla reduces long-horizon L2 error by 15.52% versus state-of-the-art VLA methods; closed-loop evaluation in NVIDIA AlpaSim improves success rate by 33.34 percentage points with significantly lower inference latency.

AdaRoboVLG: Adaptive Vision-Language Grasping via Composable Foundation Priors and Generalizable Grasp Synthesis

AdaRoboVLG is a task-adaptive Vision-Language-Grasp (VLG) framework supporting generalizable grasp synthesis across different robotic hands. Unlike VLG methods that tightly couple foundation models with end-to-end grasp policies, it learns an efficient generalizable base policy that generates and evaluates physically feasible grasp candidates through explicit kinematic mapping and force-closure-based stability estimation, while task-dependent understanding is offloaded to specialized foundation-model modules that provide composable spatial, cognitive, and temporal priors — enabling contextually adaptive grasping without retraining the underlying grasp policy. Extensive simulation and real-world experiments show efficient base-policy learning with strong cross-hand generalization, effective use of the three priors on representative grasping challenges without compromising synthesis performance versus state-of-the-art methods, and joint operation of the priors for functional grasping in cluttered and dynamic environments.

GIFT: Guided Intermediate Feature Training via Action-Oriented Structural Supervision for Robotic Manipulation

GIFT addresses the action-sufficiency gap — the mismatch between the visual richness of vision-language pretraining and what control actually needs — by supervising intermediate features with three control-relevant structures: geometry governing motion feasibility, affordances encoding instruction-relevant entities, and goals grounding instructions in task-relevant regions. The framework is architecture-flexible and was instantiated in a VLA policy, a direct-action World-Action Model, and an inverse-dynamics WAM without changing each model's action formulation. GIFT variants outperform their counterparts by 4.6/12.6/5.2 points on zero-shot LIBERO-Plus (79.6%/72.6%/87.8%) and by 12.6/9.0/8.4 points on RoboCasa, with the largest gains on articulated objects and high-precision real-world manipulation under unseen visual and spatial perturbations.

BRIDGE：面向物理 AI 的形态-控制协同设计开源人形平台

BRIDGE 是一个开源 88 厘米人形平台，由数据驱动的形态-控制协同设计框架生成：该框架以类人运动为目标联合优化人形机器人的形态。新提出的评价指标同时度量了运动重定向到人类动作的保真度与动态跟踪性能。相比 Bumi、K1、Toddlerbot 等基线人形机器人，BRIDGE 在所有报告指标上达到最先进水平，并随全身控制策略一同开源发布，展示了基础 locomotion、鲁棒平衡与高动态机动能力，同时以更优保真度复现人类运动数据。

从 1500 小时示教到在策略纠正：双臂家务操作的规模化之路

该工作开源发布 1500 小时多样化双臂家务操作示教数据，并基于专用高通量数据管线与精心设计的多阶段训练范式，在这些数据上训练了视觉-语言-动作模型 XR-2。系统实验表明其任务表现强劲，训练效率与数据利用率俱佳；沿两条规模化轴线——专家示教数据量与基于实时人工干预的 DAgger 在策略纠正后训练——任务成功率均稳步提升，在当前数据规模上呈现清晰一致的扩展趋势。数据集已开源，以支持双臂操作学习的可复现研究。

MINERVA：操作策略究竟可以做到多小还能解决 LIBERO？

MINERVA 用刻意压缩的视觉运动策略度量 LIBERO 基准的任务专属容量下限：仅 0.54M 参数的策略在四个标准 LIBERO 套件的 2000 次 rollout 中取得 95.1% 平均成功率——比论文报道的 LeRobot pi0.5 仅低 2.4 个百分点，参数量却少 7700 倍。性能在约 1M 参数处饱和，低于 0.25M 则崩溃。任务 ID 置换探针显示标准 LIBERO 指令条件化主要是在已记忆任务间做选择；该微型策略在笔记本电脑 CPU 上每控制步重规划仅需 5-9 毫秒，比 SmolVLA 快 113 倍、比 pi0.5 快 1400 倍。

LaPla：离散思维与连续动作——端到端自动驾驶的潜在对齐规划

LaPla 是一个带潜在对齐规划的视觉-语言-动作（VLA）框架，把语义理解落地为精确的运动执行。它先用残差向量量化 VAE 构建捕捉车辆运动学的动作分词器，再把该表示当作连续物理先验而非离散码本：并发动作查询在单次前向中因果地关注多模态上下文，把隐状态直接投影到预训练潜在空间，由冻结解码器转译为动作——既消除了量化误差，也绕开了自回归生成。在 nuScenes 上，LaPla 相比最先进 VLA 方法将长时域 L2 误差降低 15.52%；在 NVIDIA AlpaSim 闭环评测中成功率提升 33.34 个百分点，推理延迟显著降低。

AdaRoboVLG：基于可组合基础模型先验与通用抓取合成的自适应视觉-语言抓取

AdaRoboVLG 是一个任务自适应的视觉-语言-抓取（VLG）框架，支持跨不同机器人手的通用抓取合成。与把基础模型紧耦合进端到端抓取策略的既有 VLG 方法不同，它学习一个高效、可泛化的基础策略：通过显式运动学映射与力闭合稳定性估计来生成并评估物理上可行的抓取候选；任务相关的理解则交给专门的基础模型模块，以可组合的空间、认知与时间先验形式融入抓取合成，无需重训底层抓取策略即可实现上下文自适应的抓取。大量仿真与真实实验表明：基础策略学习高效且跨手泛化强；三类先验可分别应对代表性抓取挑战且不损害相较最先进方法的合成性能；先验联合运作可在杂乱动态环境中实现功能性抓取。

GIFT：面向机器人操作的动作导向结构化监督引导中间特征训练

GIFT 针对"动作充分性缺口"——视觉-语言预训练提供的视觉丰富性与控制真正所需信息之间的错配——提出对中间特征施加三类与控制相关的结构监督：决定运动可行性的几何、编码指令相关实体的可供性、以及把指令 grounding 到任务相关区域的目标。该框架与架构无关，已在 VLA 策略、直接动作世界动作模型（WAM）与逆动力学 WAM 上实例化，且不改动各模型原有的动作表达。在零样本 LIBERO-Plus 上，GIFT 各变体以 79.6%/72.6%/87.8% 超出对应基线 4.6/12.6/5.2 分；在 RoboCasa 上以 61.4%/83.6%/82.3% 超出 12.6/9.0/8.4 分；在关节物体任务与受未见视觉/空间扰动的高精度真实操作中增益最大。

Spatially Aware World Action Model via Geometric Latent Diffusion

SA-WAM repurposes a pretrained video diffusion model into a spatially aware World Action Model that predicts actions, RGB, and depth within a single diffusion backbone. A nonlinear depth encoding maps unbounded depth into the bounded input domain of the frozen VAE tokenizer, adding geometric awareness without 3D-specific fine-tuning. SA-WAM achieves state-of-the-art results on RoboCasa and LIBERO-Plus while improving future-state predictions, and outperforms strong baselines on a real UR5 arm with large gains in randomized environments.

World-Model-Augmented Visual Locomotion for Humanoids on Foothold-Constrained Terrain

WM-LOCO jointly trains a recurrent world model and a PPO policy so a humanoid can traverse foothold-constrained terrain — stepping stones, gaps, and narrow stair treads — using only proprioception and a single onboard depth image, with no explicit foothold labels. In simulation it succeeds on gaps and stepping stones where a matched baseline fails completely, and the same policy deployed on a physical Unitree G1 traverses all three terrain classes with a 93.3% average success rate.

ZETA: A Controlled Study of Zero-Shot Cross-Embodiment VLA Transfer for Tabletop Manipulation

ZETA is the first controlled study of zero-shot cross-embodiment transfer for Vision-Language-Action models. It separates strict zero-shot transfer (target embodiment absent from all training data) from pretrain-exposed zero-shot transfer (target embodiment seen only in pretraining), and introduces a benchmark spanning 14 held-out target embodiments in simulation and the real world. Controlled factor analysis shows local end-effector representations (+15pp), source embodiment diversity (+18pp), and auxiliary co-training (+7pp) all help, while adding just 5% target-embodiment data during pretraining improves target-embodiment progress by 13.4 percentage points.

SA-WAM：几何潜在扩散的空间感知世界动作模型

SA-WAM 将预训练视频扩散模型改造为空间感知的世界动作模型：在单一扩散骨干内同时预测动作、RGB 与深度。非线性编码把无界的深度信号映射到冻结 VAE tokenizer 的有界输入域，无需三维微调即可引入几何信息。SA-WAM 在 RoboCasa 与 LIBERO-Plus 上取得最先进结果，同时改善未来状态预测，并在真实 UR5 机械臂上优于强基线，在随机化环境中增益尤为显著。

WM-LOCO：立足点受限地形上的世界模型增强人形机器人运动

WM-LOCO 联合训练循环世界模型与 PPO 策略，使人形机器人仅凭本体感觉与单张机载深度图即可穿越立足点受限地形——踏脚石、沟隙与窄台阶——全程无需显式落足标签。仿真中它在沟隙与踏脚石上成功通过（匹配基线完全失败）；同一策略部署到实体 Unitree G1 后，在全部三类地形上平均成功率达 93.3%。

ZETA：桌面操作零样本跨本体 VLA 迁移的受控研究

ZETA 是对 VLA（视觉-语言-动作）模型零样本跨本体迁移的首个受控研究：它区分“严格零样本”（目标本体完全不出现在训练数据中）与“预训练暴露式零样本”（目标本体仅在预训练中出现），并提出覆盖仿真与真机、含 14 个留出目标本体的评测基准。受控因素分析显示：局部末端执行器表征（+15 个百分点）、源本体多样性（+18）与辅助共训练（+7）均有助益；预训练中仅加入 5% 目标本体数据即可使目标本体进度提升 13.4 个百分点。

Non-Prehensile Throwing: A Reinforcement Learning Perspective

This paper casts non-prehensile (grasp-free) throwing as a reinforcement learning problem: the policy explicitly exploits sliding and rolling contact modes and directly optimizes joint-space trajectories, requiring no analytical contact models or custom parameterizations. The policy reaches 99% simulated success across thousands of configurations and generalizes to unseen objects; deployed zero-shot on a UR5e running near its physical limits at 5 m/s end-effector velocity, it throws heavy 790 g and large 20x20x28 cm objects to targets up to 350 cm away or 180 cm high with a 97% real-world success rate.

A System for Fast, Resilient, and Adaptable Loco-Manipulation Behaviors on Humanoid Robots

This paper presents a robot-local, runtime-editable behavior authoring and runtime system that treats behavior architecture as a primary enabler of humanoid capability: object-centric Affordance Templates provide organization and logic, a behavior scene with primitive scene actions makes perception editable at runtime, and a continuously synchronized operator interface supports runtime authoring, monitoring, and repair. Demonstrated on Unitree H1-2 and Alex across six task variants, the system executes a push-door traversal in 34 seconds and color-sorts six balls under human disturbance in 45 seconds, with new loco-manipulation behaviors authored from scratch in hours.

Facet-0: A Robotic Foundation Model for Contact-Rich Precise Manipulation

Facet-0 is a robotic foundation model that predicts and values the contact consequences of its actions: it aligns a causal wrench history with vision-language semantics and kinematic state, uses flow matching to generate action chunks jointly with the wrist-wrench profile they are expected to induce, and concentrates reinforcement learning on decisive interactions via a distributional Action-Wrench Critic and contact-selective credit. Trained on ManuFacet-1K, a 1,000-hour force-synchronized corpus spanning three embodiments, it reaches 82% mean success on five sub-millimeter computer-assembly tasks versus 15% for the strongest baseline, with 0.5 mm placement accuracy and 50 ms command latency.

非抓取抛掷：一种强化学习视角

本文用强化学习解决非抓取（无抓取）抛掷问题：策略显式利用滑移与滚动接触模式，直接优化关节空间轨迹，无需解析接触模型或自定义参数化。策略在仿真中达到数千构型 99% 的成功率并能泛化到未见物体；零样本部署到 UR5e 后，以接近物理极限的 5 m/s 末端速度，可将 790 g 重物与 20×20×28 cm 大物体抛至最远 350 cm、最高 180 cm 的目标，真实世界成功率 97%。

面向人形机器人的快速、鲁棒、可适配移动操作行为系统

本文提出一套机器人本地的、运行时可编辑的行为编排与运行系统，把人形机器人的移动操作能力建立在"行为架构"之上：以物体为中心的 Affordance Templates 提供组织与逻辑，行为场景与原始场景动作实现运行时可编辑感知，操作员界面与机器人持续同步以支持运行时编写、监控与修复。系统在 Unitree H1-2 与 Alex 上完成六种任务变体演示，推门穿越 34 秒、人类干扰下按颜色分拣六个球 45 秒，新行为可在数小时内从零创建。

Facet-0：面向接触丰富精密操作的机器人基础模型

Facet-0 是一个会"预测并评估接触后果"的机器人基础模型：它把视觉-语言语义、运动学状态与因果腕力历史对齐，用流匹配同时生成动作块与预期诱发的腕部力/力矩曲线，并通过分布式的 Action-Wrench Critic 与接触选择性奖励把强化学习集中在决定性交互上。在 1,000 小时力同步语料 ManuFacet-1K 上训练后，该系统在五项亚毫米计算机装配任务上取得 82% 的平均成功率（最强基线仅 15%），放置精度 0.5 mm、指令延迟 50 ms。

LightNav-0: Eliciting VLM Spatial Intelligence for Generalist Embodied Navigation

LightNav-0 is a compact generalist navigation model that elicits the spatial intelligence of a pretrained VLM and aligns it with navigation, without task-specific prediction heads. A unified token interface expresses task-, scene-, and embodiment-agnostic spatial intent through dual-channel pointing, while a residual vector-quantized action tokenizer maps that intent to precise, embodiment-specific trajectories. Trained on 2K+ scenes and 4K+ hours of embodied navigation data, LightNav-0 achieves state-of-the-art monocular success rates across all 10 public navigation simulation settings and demonstrates zero-shot generalization across robot embodiments and dynamic targets in the real world.

Driving on Memory

This study probes how much of a NAVSIM score actually comes from reacting to the dynamic traffic scene. The authors remove a driving model's camera input and replace it with memories from prior drives at the same location — persistent scene information such as road layout, but no current traffic state. Memory alone reaches or even exceeds leading end-to-end methods on NAVSIM, showing that a high NAVSIM score does not require reacting to the evaluated scene. The effect is benchmark-dependent: driving from memory causes substantially larger drops on Bench2Drive and RealEngine. Code is released.

SUN: Persistent Programs For Language-Grounded Control-to-Learning-to-Real Policies

Kuafu introduces Semantically UNified (SUN) Programs — typed executables that define geometric and contact relations once and compile them into aligned MPC costs, RL rewards, satisfaction predicates, transition guards, and diagnostics. A large vision-language system synthesizes SUN Programs from language and scene semantics, MPC screens feasibility, and the retained semantics train stage-conditioned policies. Across nine tasks Kuafu reaches 82.03% macro-success (vs. 35.67% sparse-reward and 24.75% Stage-BC baselines), generates 10.57x more successful trajectory time per teleoperation hour at 8192-way scale, and its 500-trajectory datasets train DP3 policies to 46.0% simulation and 34.7% physical Franka/Kinova success — all without demonstrations or dense rewards.

LightNav-0：激发VLM空间智能的通用具身导航模型

LightNav-0是一个紧凑的通用导航模型，它激发预训练VLM的空间智能并将其与导航对齐，无需任何任务专用预测头。统一token接口通过双通道指向表达与任务、场景、形态无关的空间意图，残差向量量化动作分词器再把意图映射为精确的、形态专属的轨迹。基于2000+场景、4000+小时具身导航数据的训练，LightNav-0在全部10个公开导航仿真设置上取得最先进的单目成功率，并在真实世界中展示了跨机器人形态、跨场景、面向静态与动态目标的零样本泛化能力。

Driving on Memory：仅凭记忆完成驾驶评测

这项研究探明了一个问题：NAVSIM分数中有多少真正来自对动态交通场景的反应。作者移除驾驶模型的摄像头输入，改用同一地点先前驾驶的记忆——即道路布局等持久场景信息，但不含任何当前交通状态。仅凭记忆，模型在NAVSIM上就能达到甚至超越领先的端到端方法，说明高分并不需要模型对被测场景作出反应。该效应依赖具体基准：在Bench2Drive与RealEngine上，仅凭记忆驾驶会造成显著更大的性能下降。代码已开源。

SUN：面向语言接地控制—学习—实机策略的持久化程序

Kuafu提出语义统一（SUN）程序——一种类型化可执行程序，几何与接触关系只需定义一次，即可编译为对齐的MPC代价、满足谓词、RL奖励、转移守卫与诊断信号。大型视觉语言系统从语言与场景语义自动合成SUN程序，MPC先进行可行性筛查，保留的语义再用于训练分阶段策略。在九个任务上Kuafu取得82.03%的宏观成功率（稀疏奖励基线35.67%、Stage-BC基线24.75%），8192路并行规模下每小时遥操作产生的成功轨迹时间是传统方式的10.57倍；每个任务500条轨迹训练出的DP3策略在仿真中达到46.0%、在真实Franka与Kinova机械臂上达到34.7%的成功率——全程无需示范数据或人工稠密奖励。

Riemann-1.0: An Embodied World Action Model for Physical AI

Riemann-1.0 is a fully causal autoregressive World Action Model that jointly models multi-view visual observations, robot states, and embodiment-specific actions as causal state transitions in a single sequence, functioning as both an executable robot policy and a multi-embodiment visual world simulator. Progressive embodied pretraining over 200K+ hours of human and robot interaction data yields 94.3% on RoboTwin2.0, 99.0% on LIBERO, 62.6% on RoboCasa-365 (+8.4% over previous best), and 85.0% SR / 94.4% PSR on long-horizon real-world manipulation, exceeding the strongest open-source baseline by 15% SR.

CLAP: Cross-Embodiment Video World Models are Zero-Shot Physical Simulators

CLAP trains action-conditioned video world models on internet-scale heterogeneous video spanning humans and diverse robots, reconciling incompatible action spaces via end-effector poses, language instructions, and latent actions under a curriculum-based cross-embodiment learning recipe. It approaches or surpasses single-embodiment state of the art on DROID, compounds gains through few-shot adaptation, and ships the most comprehensive suite of action-conditioned video world models to date, with all code and models open-sourced.

Riemann-1.0：面向物理AI的具身世界动作模型

Riemann-1.0是一个完全因果的自回归世界动作模型（WAM），将多视角视觉观测、机器人状态与形态特定动作作为因果状态转移统一建模在单一序列中，同一模型既可在线执行机器人策略，又可充当多形态视觉世界模拟器。基于20万+小时人类与机器人交互数据的渐进式具身预训练，在RoboTwin2.0上达到94.3%、LIBERO上99.0%、长时程组合基准RoboCasa-365上62.6%（较此前最优提升8.4%）；真实世界长时程操作任务成功率达85.0%、进度成功率达94.4%，较最强开源基线高出15个百分点。

CLAP：跨形态视频世界模型，零样本的物理模拟器

CLAP在互联网规模的异构视频（涵盖人类与多种机器人）上训练动作条件视频世界模型，通过末端执行器位姿、语言指令与潜在动作三种表征统一互不兼容的动作空间，并采用基于课程学习的跨形态训练范式：先用潜在动作从无标注视频学习基础物理先验，再将其接地到末端执行器动作空间实现零样本部署。在DROID等挑战性环境中达到或超越单形态SOTA，并通过少样本适应进一步放大优势；所发布套件是目前覆盖动作条件空间与机器人形态最全面的视频世界模型，代码与模型全部开源。

Beyond Imitation: Self-Improving Robot Policies via Off-Policy Q-Planning

Q-Planning equips a large visuomotor behavior-cloning policy with a small off-policy Q-function, enabling value-guided action selection at inference and online self-improvement that fine-tunes only the Q-function. On LIBERO and bimanual RoboTwin, ten self-improvement iterations lift every benchmark score (LIBERO-10 93% to 99%, RoboTwin 83.8% to 91.4%); on two contact-rich bimanual real-robot tasks the same loop improves purely from its own deployment rollouts (stack-cups 40% to 90%, insert-wallet 25% to 80%), whereas filtered SFT on successful rollouts stalls at 55% and 30%.

Beyond Imitation：基于离策略Q-规划的自改进机器人策略

Q-Planning为大规模视觉运动行为克隆策略配备一个小型离策略Q函数，推理时用Q加权选择动作，在线自改进时只微调Q函数而不动策略权重。在LIBERO与双臂RoboTwin上，十轮自改进迭代提升所有基准分数（LIBERO-10从93%升至99%，RoboTwin从83.8%升至91.4%）；在两个接触密集的双臂真实机器人任务中，同一循环仅凭部署rollout即可自改进（叠杯从40%升至90%、插钱包从25%升至80%），而仅用成功rollout的SFT停滞在55%和30%。

BrainWAM: Action-Space Coordination of Semantic Priors and Predictive Dynamics for Autonomous Driving

BrainWAM is a structured action-space coordination framework that unifies semantic reasoning (VLA priors) and predictive dynamics (world models) for end-to-end autonomous driving, reaching state-of-the-art 89.5 PDMS on NAVSIM v1 and 89.6 EPDMS on NAVSIM v2 while shortening inference latency via asynchronous rectified-flow inference.

DreamX-Phi 1.0: Action-Conditioned Video World Model for Robotic Manipulation

DreamX-Phi 1.0 is an action-conditioned video world model that predicts future observations for robotic manipulation from a frame, a language instruction, and an action sequence, using per-arm SE(3) geometric encoding, a depth branch, and SAM3+V-JEPA object-consistency constraints; it ranked first on Track 1 and second on Track 2 of the WorldArena 2.0 Challenge.

HumanTracker: Towards Comprehensive and Human-Aligned Motion Tracking Benchmark

HumanTracker is a large-scale benchmark containing approximately 153 hours of professional optical motion trajectories for evaluating humanoid motion tracking, paired with HumanScore, a preference-aligned metric trained on 12K motion pairs that better predicts human perception and reveals contact and stability failures that kinematic metrics miss.

DreamX-Phi 1.0：面向机器人操作的动作条件视频世界模型

DreamX-Phi 1.0是一种动作条件视频世界模型：给定观测帧、语言指令与动作序列（末端位姿与夹爪状态），预测未来的观测结果；通过逐臂SE(3)几何编码、深度分支与SAM3+V-JEPA物体一致性约束保证预测忠实度，在WorldArena 2.0挑战赛中获赛道一第一、赛道二第二。

HumanTracker：面向全面且符合人类感知的人形运动跟踪基准

HumanTracker是一个大规模基准，包含约153小时来自多位专业表演者的光学运动轨迹，用于评估人形运动跟踪；配套提出HumanScore——一种基于1.2万对运动数据训练、与人类偏好对齐的指标，能更好地预测人类感知，并揭示运动学指标容易忽略的接触与稳定性失败。

BrainWAM：面向自动驾驶的语义先验与预测动力学动作空间协同

BrainWAM是一种结构化的动作空间协同框架，将语义推理（VLA先验）与预测动力学（世界模型）统一用于端到端自动驾驶，在NAVSIM v1上达到89.5 PDMS、NAVSIM v2上达到89.6 EPDMS的领先水平，并通过异步整流流推理缩短推理延迟。

StellaVLA: In-Context Structured Demonstration for Generalizable Vision-Language-Action Models

StellaVLA adapts at test time by conditioning on a single retrieved demonstration: an automated offline pipeline converts raw trajectories into structured demonstrations (task plans, sub-goal descriptions, verbalized 3D motion) that let the policy reason about the task rather than mimic pixels. It ranks first on the VLA-Arena leaderboard (Aug 1, 2026) with 0.63 vs 0.44 for pi0.5, reaches 98.8% on LIBERO, and adds zero inference latency.

G0.5: One Autoregressive Stream for Robot Reasoning and Action

G0.5 unifies robot reasoning and action in a single autoregressive transformer decoder that emits reasoning and action tokens under one objective, surpassing state-of-the-art models across 7 independent regimes, including real-robot fine-tuning (76.7% vs 53.3% for pi0.5 on R1lite/R1pro), the 2025 BEHAVIOR Challenge, DROID zero-shot transfer (82.5%), and LIBERO (98.9%).

StellaVLA：上下文结构化演示驱动的可泛化视觉-语言-动作模型

StellaVLA在测试时仅凭一条检索到的演示即可完成分布外适应：离线管线将原始轨迹自动转化为任务计划、子目标描述与口语化3D动作等结构化演示，作为上下文指导让策略“理解任务”而非“模仿像素”。在VLA-Arena排行榜（2026-08-01）以0.63总分登顶（π0.5为0.44），LIBERO平均成功率98.8%，且推理阶段零额外延迟。

G0.5：单自回归流统一机器人推理与动作生成

G0.5将推理与动作生成统一到单一自回归Transformer解码器中，推理token与动作token在同一目标下联合生成；在7个独立评测场景中全面超越现有SOTA，包括真实机器人微调（R1lite/R1pro上76.7%对π0.5的53.3%）、2025 BEHAVIOR挑战赛、DROID零样本迁移（82.5%）与LIBERO（98.9%）。

Flex-π: A Multi-Stream World-Action Model with Compute Flexibility

Flex-π is a 6B-parameter world-action model that jointly denoises 3D geometry, object-centric semantics, RGB, and actions in a shared latent space, using a frozen video VAE that encodes pointmaps almost losslessly — achieving 2-7x gains over strong baselines on dexterous real-world bimanual manipulation while running faster than π0.5.

Hidden in Plain Sight: Diffusion-Based Unrestricted Robotic Attacks on Vision-Language-Action Models

DURA generates visually natural adversarial patches for Vision-Language-Action models via diffusion-based unrestricted attacks, working in both white-box and black-box settings and steering robots toward attacker-specified actions in simulation and the physical world.

Surgical WAM: A World-Action Model for Data-Efficient Surgical Robot Learning

Surgical WAM pretrains a generative world-action model on action-free endoscopic video, then fine-tunes it on a fixed budget of action-labeled demonstrations, improving average success from 63.5% to 77.8% across four simulated surgical manipulation tasks.

MiDAS: Adaptation of Generalist Robot Policies with Minimal Data

MiDAS studies minimal-data adaptation: a pretrained policy learns a new task from as little as one demonstration, anchored by behavior cloning and then improved through value-based online RL on a residual policy parameterization. It recovers strong performance on LIBERO and RoboCasa from a single demo and improves a fragile policy on a bimanual YAM platform over ~6 hours of online interaction — per the paper, the first reliable robot policy adaptation from a single task demonstration.

Flex-π：具备计算弹性的多流世界-动作模型

Flex-π是一个60亿参数的世界-动作模型，在共享潜空间中联合去噪3D几何、物体中心语义、RGB与动作信号；利用冻结的视频VAE几乎无损地编码点图，在灵巧的真实世界双臂操作任务上相较最强基线取得2-7倍提升，且推理速度快于π0.5。

藏在明处：面向视觉-语言-动作模型的扩散式无限制机器人攻击

DURA通过基于扩散模型的无限制攻击为视觉-语言-动作模型生成视觉上自然的对抗补丁，支持白盒与黑盒两种设置，能在仿真与真实物理世界中引导机器人执行攻击者指定的动作。

Surgical WAM：面向数据高效手术机器人学习的世界-动作模型

Surgical WAM先在无动作标注的内镜视频上预训练生成式世界-动作模型，再在固定预算的动作标注演示上微调，在四个模拟手术操作任务上将平均成功率从63.5%提升至77.8%。

MiDAS：单条演示即可启动的通用机器人策略最小数据适应

MiDAS提出“最小数据适应”范式：预训练策略仅需一条演示，经行为克隆锚定后再通过残差策略上的基于价值的在线强化学习自主提升。在LIBERO与RoboCasa上仅凭单条演示即恢复强任务性能，并在双臂YAM平台上用约6小时在线交互将脆弱策略提升为鲁棒策略——据论文所述，这是首次实现可靠的单条演示策略适应。

FactorDrive: Adaptive Multi-Step Reasoning Driven by Planning-Critical Factors for End-to-End Autonomous Driving

FactorDrive is an end-to-end autonomous driving framework that grounds planning reasoning in trajectory-relevant spatial-physical evidence and adapts reasoning depth to scene-specific planning demands. It builds on large-scale driving-domain instruction tuning, constructs PCF-CoT, a chain-of-thought dataset organized around planning-critical factors (PCFs), and introduces Quality Search-Guided Group Relative Policy Optimization (QS-GRPO), which uses Monte Carlo Tree Search guided by trajectory-level planning rewards to discover higher-quality reasoning paths and optimize the policy via GRPO. Experiments on open-loop nuScenes and closed-loop-oriented NAVSIM benchmarks demonstrate state-of-the-art planning performance.

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation

SLIM is a compact 0.5B-parameter latent interaction policy that learns action-grounded predictive latents — representations capturing both action-conditioned future transitions and the actions that explain observed changes — through self-supervised masked trajectory prediction combining action reconstruction with future-latent prediction. A compact Mixture-of-Transformers backbone models interactions between observation latents and action tokens, and the policy is trained with flow matching for language-conditioned action generation. Across simulation benchmarks and real-world evaluation, SLIM matches or exceeds representative large-scale VLA and world-action-model baselines with fewer parameters, no additional embodied pretraining, lower inference latency, and substantially lower GPU memory usage.

RynnValue: Scaling Robotic Value Foundation Models with Temporal Distance

RynnValue is an open-source value foundation model for robotic manipulation that replaces preference or progress annotations with temporal distance — the directed cost-to-go from an observation to a language-specified goal — as the supervision target. Trained on over 7,000 hours and roughly 3 million instruction-conditioned clips without preference labels, it attains a Kendall's tau_a of 0.675 on RBM-EVAL-OOD, surpassing the fully preference-supervised state of the art (0.655) and more than doubling a progress-only baseline (0.292), while generalizing zero-shot to unseen tasks, embodiments, and viewpoints. Converted into dense rewards via potential-based shaping, it lifts real-world policy success from 52.5% to 72.5% online and from 63.8% to 82.5% offline.

FactorDrive：由规划关键因子驱动的自适应多步推理端到端自动驾驶

FactorDrive 是一个端到端自动驾驶框架，将规划推理锚定在与轨迹相关的空间-物理证据上，并根据场景特定的规划需求自适应推理深度。它以大规模驾驶域指令微调为基础，构建了围绕规划关键因子（PCF）组织的 PCF-CoT 思维链数据集，并提出 QS-GRPO：用轨迹级规划奖励引导蒙特卡洛树搜索（MCTS）发现更高质量的推理路径，再通过 GRPO 优化策略。在开环 nuScenes 与面向闭环的 NAVSIM 基准上的大量实验表明，FactorDrive 取得了最先进的规划性能。

SLIM-0.5B：面向机器人操作的行动锚定预测隐空间学习

SLIM 是一个仅 0.5B 参数的紧凑隐空间交互策略，通过自监督掩码轨迹预测学习行动锚定的预测隐变量——同时刻画行动条件下的未来状态转移与能解释观测变化的行动——结合动作重建与未来隐变量预测。紧凑的 Mixture-of-Transformers（MoT）骨干网络建模观测隐变量与动作 token 之间的交互，策略采用流匹配训练以生成语言条件下的动作。在仿真基准与真实世界评估中，SLIM 以更少的参数、无需额外的具身预训练、更低的推理延迟与显著更低的 GPU 内存占用，达到或超过代表性的大规模 VLA 与世界行动模型基线。

RynnValue：用时间距离扩展机器人价值基础模型

RynnValue 是一个开源的机器人操作价值基础模型，用时间距离——从观测到语言指定目标的定向代价——替代偏好或进度标注作为监督信号。该方法无需偏好标注，在超过 7,000 小时、约 300 万条指令条件片段上训练，在 RBM-EVAL-OOD 上取得 0.675 的平均 Kendall tau_a，超过完全偏好监督的最先进方法（0.655），并比仅用进度的基线（0.292）提升一倍以上，且能零样本泛化到未见任务、具身与视角。通过基于势能的奖励整形转为稠密奖励后，真实世界策略成功率在线从 52.5% 提升到 72.5%，离线从 63.8% 提升到 82.5%。

AtlasVLA: Persistent World-Ego State Modeling for Vision-Language-Action Models

AtlasVLA addresses the reactive limitations of Vision-Language-Action (VLA) models in partially observable, long-horizon tasks by introducing a dual-memory architecture: a 4D Persistent World State Memory that lifts transient 2D observations into a globally updated voxel-hashed spatial state, and an Ego-Working State Memory that tracks historical ego state and task progress. A diffusion transformer is conditioned on this joint world-ego state for proactive reasoning. Using only a single wrist-mounted camera, AtlasVLA achieves state-of-the-art results on LIBERO, RLBench, and real-world benchmarks, outperforming multi-view baselines by 9.4% on LIBERO-Long and 17.5% in real-world long-horizon tasks.

Detection and Ranging of Transient Extrinsic Contacts Based on 6D Dynamic Tactile Sensing

TECDAR detects and localizes transient extrinsic contacts (collisions between a grasped object and the environment) using dynamic tactile sensing built on a single 2.5x3 mm 6D inertial measurement unit in each gripper tip. The sensor captures sub-millisecond tip deformations at 7 kHz while streaming only 84 KB/s; fusing tactile data with robot pose through an extended Kalman filter localizes contacts to roughly 7 mm accuracy within 180 ms. This near-instantaneous localization lets a robot correct its trajectory on a millisecond scale, enabling precise tool manipulation and touch-driven exploration for precision assembly, surgical assistance, and autonomous exploration.

Learning Fault-Tolerant Locomotion with Adaptive Gait Timing

This work proposes a deep reinforcement learning approach for fault-tolerant quadruped locomotion under actuator power loss. An asymmetric actor-critic architecture lets the critic use privileged information during training while the actor reconstructs a corresponding latent representation from proprioception, enforced by a latent-alignment loss. A learnable gait frequency parameter augments the action space, enabling adaptive gait timing under terrain variation and actuator degradation without predefined faulty-leg strategies. The method is validated in high-fidelity simulation on uneven terrain and in real-world experiments on flat ground using a 68 kg quadruped.

AtlasVLA：面向视觉-语言-动作模型的持久世界-自我状态建模

AtlasVLA 针对视觉-语言-动作（VLA）模型在部分可观测、长时程任务中的反应式局限，提出双记忆架构：4D 持久世界状态记忆将瞬态 2D 观测提升为全局更新的体素哈希空间状态，自我工作状态记忆则追踪历史自我状态与任务进度；扩散 Transformer（DiT）以该联合世界-自我状态为条件进行主动推理。仅使用单个腕部相机，AtlasVLA 即在 LIBERO、RLBench 与真实世界基准上取得 SOTA 成绩，在 LIBERO-Long 上超出多视角基线 9.4%，在真实长时程任务上超出 17.5%。

基于 6D 动态触觉传感的瞬时外部接触检测与测距

TECDAR 利用嵌在夹爪指尖的单个 2.5×3 mm 6D 惯性测量单元（IMU）实现动态触觉传感，用于检测并定位被抓物体与环境之间的瞬时外部接触。传感器以 7 kHz 采样率捕捉亚毫秒级的指尖形变，而数据流仅为 84 KB/s；将触觉数据与机器人位姿经扩展卡尔曼滤波融合后，可在 180 ms 内将接触定位到约 7 mm 的精度。这种近乎实时的定位使机器人能在毫秒尺度上修正轨迹，为精密装配、手术辅助以及依赖触觉探索的自主作业提供了关键能力。

学习自适应步态时序的容错运动控制

本文提出一种面向执行器断电故障的深度强化学习容错四足运动控制方法。非对称 actor-critic 架构让 critic 在训练时使用特权信息，而 actor 仅从本体感觉观测中重建对应的潜在表征，并通过潜在对齐损失保证两者一致。动作空间被扩充了一个可学习的步态频率参数，使策略能够根据地形变化与执行器退化自适应调整步态时序，而无需预定义故障腿策略。该方法在崎岖地形的高保真仿真与 68 kg 四足机器人的平地真实实验中均得到验证。

Hijacking Robots with a Piece of Paper: A Systematic Study of Physical Prompt Injection in VLM-Controlled Robots

This study shows that adversarial text placed inside a robot's visual field can act as an indirect prompt injection against VLM-controlled manipulation. Across 5,670 trials on GPT-4o, Gemini 2.5 Flash, and Qwen3-VL-32B, physical signage attacks succeeded at 27.0%, 29.4%, and 5.0% respectively, with authority-impersonation and negation attacks transferring across all three models. Simple defenses — prompt hardening, two-stage verification, and text masking — reduce risk substantially (up to 100%), though each carries trade-offs.

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation

DyPES-VLA is a cross-embodiment vision-language-action framework that separates shared dynamics priors from embodiment-specific control. A future-prediction objective trains the vision-language backbone to capture object motion, contact, and interaction-induced scene changes across heterogeneous data, while an embodiment-specific Mixture-of-Experts action head translates those priors into actions directly in each robot's native action space, eliminating manual action pre-alignment. As a single generalist policy it reaches 98.0% success on LIBERO, 59.25% on RoboCasa-GR1, and 89.02% on RoboTwin 2.0.

ω-0: A Latent Predictive World Action Model for Concurrent Humanoid Loco-Manipulation

ω-0 is a whole-body world-action model that lets a single humanoid policy move, rebalance, and manipulate objects concurrently from a language instruction and visual observation. Instead of reconstructing future videos, it learns compact latent foresight embeddings coupled to diffusion-based whole-body action generation, and uses controller-based simulation replay to ground human motion priors into executable action latents. Trained on the new 40+ hour ω-HOME dataset, one model produces smooth manipulate-while-moving behaviors across 11 real-world household tasks, consistently beating imitation learning, VLA, humanoid, and world-action-model baselines.

一张纸劫持机器人：VLM 控制机器人物理提示注入的系统性研究

该研究证明，放置在机器人视野内的对抗性文字可作为针对 VLM 操控的间接提示注入。在 GPT-4o、Gemini 2.5 Flash 与 Qwen3-VL-32B 上进行的 5,670 次试验中，物理标识攻击分别以 27.0%、29.4% 与 5.0% 的成功率得手，其中权威冒充与否定类攻击可跨三种模型迁移。简单的防御手段——提示加固、两阶段验证与文本掩码——可将风险大幅降低（最高达 100%），但各有取舍。

DyPES-VLA：面向跨本体操控的共享动力学先验与本体特定控制学习

DyPES-VLA 是一种跨本体视觉-语言-行动（VLA）框架，将共享的动力学先验与本体特定的控制解耦。未来预测目标使视觉-语言骨干在异构数据上捕获物体运动、接触与交互引起的场景变化；本体特定的混合专家（MoE）动作头则直接在每种机器人的原生动作空间中把这些先验转化为可执行动作，免去手动动作对齐。作为单一通用策略，它在 LIBERO 上达到 98.0% 成功率，在 RoboCasa-GR1 上为 59.25%，在 RoboTwin 2.0 上为 89.02%。

ω-0：面向人形机器人同步移动-操控的隐空间预测世界行动模型

ω-0 是一种全身世界行动模型，使单一人形机器人策略能够根据语言指令和视觉观测，在移动、调整姿态、保持平衡的同时完成物体操控。它不重建未来视频，而是学习紧凑的隐空间未来观测嵌入，并与基于扩散的全身动作生成耦合，再通过基于控制器的仿真回放将人类运动先验落地为可执行的行动隐变量。基于新构建的 40 多小时 ω-HOME 数据集，单一模型即可在 11 项真实家庭任务上生成流畅的边移动边操控行为，持续超越模仿学习、VLA、人形机器人与世界行动模型基线。

PRIMAL3: Pathfinding via Reinforcement and Imitation Multi-Agent Learning — Leveraging LaCAM3

PRIMAL3 is an ultra-large-scale learning-based multi-agent pathfinding framework combining reinforcement learning, topology-aware communication, LaCAM3-guided training, and PIBT-based action refinement. It targets failures at topologically critical states such as bottlenecks and dead ends, scales to city-level instances with up to 100,000 agents, substantially outperforms state-of-the-art learning-based baselines, and is validated on physical robotic systems.

DreamWAM: Beyond RGB Future Prediction for World Action Models

DreamWAM reformulates future prediction in world action models as structured modeling beyond RGB — jointly predicting appearance, motion, geometry, and semantics during training while staying RGB-only at inference. It improves matched RGB-only baselines on LIBERO (97.30% to 98.40%), shows much larger gains under unseen LIBERO-Plus perturbations (51.36% to 63.44%), and lifts real-world success from 55.6% to 74.4% across unseen lighting, background, and layout changes. Code and models are publicly released.

BridgeVLA++: A Data-Efficient, Generalizable, and Memory-Augmented Vision-Language-Action Framework for 3D Manipulation

BridgeVLA++ equips the data-efficient BridgeVLA 3D manipulation framework with a unified spatio-temporal memory that models persistent spatial context and temporal interaction history, enabling policies to reason over observation histories without sacrificing data efficiency or generalization. It achieves state-of-the-art results on two memory-dependent manipulation benchmarks, works in bimanual settings, and is validated on an additional real-world robotic platform.

PRIMAL3：借助 LaCAM3 的大规模多智能体强化与模仿学习路径规划

PRIMAL3 是一个超大规模的学习式多智能体路径规划（MAPF）框架，融合了强化学习、拓扑感知通信、LaCAM3 引导训练与基于 PIBT 的动作精化。它专门针对瓶颈、死胡同、持续冲突等拓扑关键状态下的失败，可扩展至多达 10 万个智能体的城市级实例，显著优于现有最优的学习式基线，并已在真实机器人系统上完成验证。

DreamWAM：超越 RGB 的未来预测——世界动作模型的新范式

DreamWAM 将世界动作模型中的未来预测重构为超越 RGB 的结构化建模——训练时联合预测外观、运动、几何与语义，推理时仍只依赖 RGB。它在 LIBERO 上将匹配的 RGB-only 基线从 97.30% 提升至 98.40%，在未见过的 LIBERO-Plus 扰动下增益更为显著（51.36% 提升至 63.44%），并在真实世界中把平均成功率从 55.6% 提升至 74.4%（面对未见过的光照、背景与物体布局变化）。代码与模型已开源。

BridgeVLA++：具备数据高效、强泛化与记忆增强能力的 3D 操作视觉-语言-动作框架

BridgeVLA++ 在数据高效的 BridgeVLA 三维操作框架之上引入统一的时空记忆架构，同时建模持久的空间上下文与时间交互历史，使策略能够基于观测历史进行推理，且不牺牲数据效率与泛化能力。该方法在两个依赖记忆的操作基准上达到当前最优，支持双臂操作场景，并在另一套真实机器人平台上完成验证。

PhyAI: Real-Time Physical AI at the Edge, Scalable Rollouts in the Cloud

PhyAI is a unified Physical AI inference engine with a single runtime for vision-language-action (VLA) and world-action models (WAMs) across onboard, edge, and cloud deployments. Model adapters encapsulate architecture-specific conditioning, solver, cache, and output logic while graph execution, kernels, and memory management are shared, yielding 1.40x–4.65x speedups over the official implementations of pi0, pi0.5, GR00T N1.7, and MiniCPM-Robot, plus a 2.08x latency reduction on Cosmos3-Nano-Policy-DROID. The paper also introduces the control-time Roofline to distinguish inference-bound from environment-bound control.

Bimanual Manipulation Within an 8 GB Budget: Zero-Copy Sensing and Quantized ACT on an Entry-Level Jetson

This paper runs a complete bimanual SO-101 pick-and-place system — sensing, policy, and control — on an entry-level Jetson Orin Nano Super (8 GB), using a desktop GPU only for offline training. A GStreamer/NVMM zero-copy pipeline cuts worst-case latency from 117.31 ms to 101.52 ms and peak single-core CPU from 98.0% to 77.0%; TensorRT conversion brings ACT inference from 114.02 ms to 17.93 ms in FP16 (6.4x) and 12.65 ms in INT8 (9.0x) with task success preserved (19/20, 18/20, 19/20). Two undocumented ACT findings: INT8 calibration quantizes the ResNet18 backbone but accepts zero of 145 transformer layers, and quantization need is conditional on action-chunking configuration.

GORDON: Graph-based Object-centric Rewards for Decomposition of Long-Horizon Manipulation

GORDON learns dense rewards from action-free video demonstrations by representing each scene as a graph of detected objects and spatial relations, then embedding those graphs with a self-supervised graph neural network into a task-aligned latent space. The resulting reward reveals stage-wise object-state transitions in long-horizon tasks, enabling automatic subtask discovery and sequentially composed policies. On seven manipulation tasks across MAGICAL and ManiSkill3 it reaches 74.4% average success on long-horizon tasks, roughly +35 p.p. over the best learned baseline and +25 p.p. over an oracle.

PhyAI：一套运行时打通物理 AI 的边缘实时推理与云端规模化部署

PhyAI 是一个统一的物理 AI 推理引擎，以单一运行时在机载、边缘与云端部署视觉-语言-动作（VLA）模型和世界动作模型（WAM）。模型适配器封装了架构相关的条件计算、求解器、缓存与输出逻辑，而图执行、算子、内存管理与并行服务全部共享，相较 pi0、pi0.5、GR00T N1.7、MiniCPM-Robot 的官方实现取得 1.40–4.65 倍加速，并在 Cosmos3-Nano-Policy-DROID 上实现 2.08 倍延迟降低。论文还提出控制时 Roofline，用于区分推理受限与环境受限的控制回路。

GORDON：基于图的物体中心奖励，实现长时程操作任务的自动分解

GORDON 从无动作的视频示教中学习稠密奖励：将每个视觉场景表示为检测物体及其空间关系构成的图，并用自监督图神经网络把这些图嵌入到与任务对齐的隐空间。该奖励的时间曲线在长时程任务中会显露出阶段式的物体状态迁移，从而支持自动子任务发现与顺序组合策略。在 MAGICAL 与 ManiSkill3 的七个操作任务上，长时程任务平均成功率达 74.4%，较最佳学习基线提升约 35 个百分点、较 oracle 提升约 25 个百分点。

8 GB 预算内的双臂操作：零拷贝感知与量化 ACT 登上入门级 Jetson

本文在入门级 Jetson Orin Nano Super（8 GB）上完整运行了一套双臂 SO-101 抓取-放置系统——感知、策略与控制全部在边缘完成，桌面 GPU 仅用于离线训练。基于 GStreamer/NVMM 的零拷贝流水线将最坏延迟从 117.31 ms 降至 101.52 ms，单核 CPU 峰值占用从 98.0% 降至 77.0%；TensorRT 转换使 ACT 推理延迟由 114.02 ms 降至 FP16 的 17.93 ms（6.4 倍）与 INT8 的 12.65 ms（9.0 倍），任务成功率保持不变（19/20、18/20、19/20）。论文还报告了两项 ACT 此前未被记录的发现：INT8 校准只量化 ResNet18 主干、对 145 个 transformer 层全部拒收；以及量化的必要性取决于动作分块配置。

StableMimic: Smooth Human-Like Recovery for Humanoid Motion Tracking - Learning Beyond the Tracking Distribution for Structured Post-Fall Behavior

StableMimic is a unified humanoid motion-tracking policy trained beyond the nominal tracking distribution so it can recover from falls. Perturbed resets around human get-up references expose prone, supine, and off-balance states; dedicated tracking and recovery experts are blended by a proprioceptive gate, and a hidden successor-state objective shapes human-like recovery without exposing reference identity to the deployed policy. On the retargeted LAFAN1 dance subset it achieves the lowest errors on all four tracking metrics among five methods, recovers 100/100 in matched push-to-fall trials, and is validated on real Unitree G1 robots.

Why Does Action Chunking Improve Behavioral Cloning Performance in Robotic Control?

This paper rigorously investigates why action chunking improves behavioral cloning. Experiments in simulation and the real world refute the dominant hypotheses (temporal consistency, horizon reduction, representation learning), showing the real benefits come from non-Markovian expressivity, reduced compounding error, and a newly identified effect called implicit ensembling. The authors match action-chunking performance without chunking by deploying policies as ensembles of randomized delays, and propose an explicit-ensemble policy class that significantly outperforms action chunking in many domains.

Ego2Robot: Scalable Robot Data Synthesis from Egocentric Human Data

Ego2Robot is a scalable pipeline that converts egocentric human manipulation videos into robot training data through action retargeting, robot-arm visual synthesis, and multi-level quality curation, producing 18,561 hours of data across 15 robot morphologies — the largest ego-to-robot dataset to date. Joint pretraining on Ego2Robot-synthesized and robot data consistently improves out-of-distribution generalization across visual appearance, scene layout, embodiment morphology, and task semantics, with benefits validated on real-robot deployment.

StableMimic：人形机器人动作跟踪的平滑类人跌倒恢复——在跟踪分布之外学习结构化倒地后行为

StableMimic 是一个在名义跟踪分布之外训练的统一人形机器人动作跟踪策略，能够在跌倒后自主恢复。围绕多个人类起身参考的扰动重置暴露了俯卧、仰卧与失衡等状态；专用的跟踪与恢复专家由本体感觉门控持续混合，隐藏后继状态目标则在不向部署策略暴露参考身份的前提下塑造类人恢复行为。在重定向后的 LAFAN1 舞蹈子集上，它在五种方法中取得全部四项跟踪指标的最低误差；在匹配的推倒实验中 100/100 次成功恢复，并在真实宇树 G1 机器人上得到验证。

动作分块为何能提升机器人控制中的行为克隆性能？

本文对动作分块（action chunking）为何能提升行为克隆性能进行了严谨剖析。仿真与真实世界实验推翻了主流假说（时间一致性、视界缩短、表征学习），指出真正收益来自更强的非马尔可夫表达能力、更低的误差累积，以及一个新识别的机制——隐式集成（implicit ensembling）。作者在完全不使用动作分块的情况下，通过将策略部署为随机延迟策略的集成，复现了分块策略的性能，并提出一种显式实例化集成的策略类，在多个领域显著超越动作分块。

Ego2Robot：从第一视角人类数据规模化合成机器人数据

Ego2Robot 是一条可扩展的数据合成流水线，通过动作重定向、机械臂视觉合成与多级质量筛选，将第一视角人类操作视频转化为机器人训练数据，产出横跨 15 种机器人形态、共计 18,561 小时的训练数据——这是迄今规模最大的 ego-to-robot 数据集。在合成数据与真实机器人数据上的联合预训练，能在视觉外观、场景布局、本体形态与任务语义等多个扰动维度上持续提升分布外泛化能力，并在真实机器人部署中得到验证。

CLIFT: Turning Gemini Robotics On-Device into Humanoid Specialists via Non-Invasive Closed-Loop Iterative Fine-Tuning

CLIFT shows how closed-weight robot foundation models can be specialized for agile, contact-rich humanoid tasks through managed supervised fine-tuning APIs. By converting deployment-time reward feedback into API-compatible supervised data, CLIFT pushes Gemini Robotics On-Device to near-perfect success after two flywheel cycles — without ever accessing weights, gradients, or losses.

BWM: A Low-Cost High-Fidelity World Simulator for Robot Learning

BWM is an open-source, low-cost, action-conditioned world model that predicts future observations for robot manipulation, serving both as a data engine that augments imitation-learning data with action-aligned rollouts and as a policy evaluator for closed-loop assessment, risk anticipation, and policy ranking. It ranks first overall in the WorldArena Challenge across Track 1 and two Track 2 applications, with checkpoints, code, and interfaces released.

WCM: A World Critic Model for Vision-Language-Action Reinforcement Learning

WCM replaces scalar-return critics in VLA reinforcement-learning post-training with a lightweight LeJEPA-based World Critic that jointly predicts future latent states and estimates values, giving the critic explicit temporal structure. Across 149 tasks on four benchmarks and seven real-world manipulation tasks, WCM delivers state-of-the-art in-distribution and out-of-distribution performance on Pi0, Pi0.5, and OpenVLA-OFT.

CLIFT：通过非侵入式闭环迭代微调，将 Gemini Robotics On-Device 打造成人形机器人专家

CLIFT 展示了如何通过受管监督微调（SFT）API，将闭源权重的人形机器人基础模型定制为敏捷、接触密集任务的专家。通过把部署期的奖励反馈转化为 API 兼容的监督数据，CLIFT 在两个飞轮周期内将 Gemini Robotics On-Device 推向近乎完美的成功率——全程无需接触权重、梯度或损失。

BWM：面向机器人学习的低成本高保真世界模拟器

BWM 是一个开源、低成本、动作条件的世界模型，可预测机器人操作任务的未来观测，既可作为数据引擎，用动作对齐的 rollout 扩充模仿学习数据，也可作为策略评估器，用于闭环评估、风险预判与策略排序。它在 WorldArena 挑战赛中 Track 1 与两个 Track 2 应用中总排名第一，并已开源模型权重、训练与推理代码及接口。

WCM：面向视觉-语言-动作模型强化学习的 World Critic 模型

WCM 用基于轻量级 LeJEPA 架构的 World Critic 取代 VLA 强化学习后训练中的标量回报评论家，在估计价值的同时联合预测未来潜在状态，为评论家赋予显式的时间结构。在四个基准的 149 个任务与 7 个真实操作任务上，WCM 在 Pi0、Pi0.5 与 OpenVLA-OFT 上均取得分布内与分布外的最优性能。

TacWAM: Anchor-Guided World Action Model with Mechanics-Aware Tactile Prediction

TacWAM is a world action model that predicts future tactile states — appearance, dense force fields, and deformation flow — to supervise contact-rich manipulation learning, reaching 75.0% average success across four real-world tasks and outperforming the strongest baseline by 37.5 percentage points.

FasTac: A Curved Multispectral Vision-Based Tactile Sensor for High-Speed High-Precision 3D Shape and Force Perception

FasTac is a curved vision-based tactile sensor combining multispectral photometric stereo, dynamic-convolution force estimation, and FPGA acceleration in a compact form. It reduces depth error to 0.0415mm MAE, estimates three-axis forces with ~2.4-2.7% NMAE, and cuts processing latency to 1.09ms on FPGA.

PAC-MAN: Perception-Aware CBF-RL for Whole-Body Safety in Humanoid Dodgeball

PAC-MAN couples control-barrier-function safety with deployment-realistic onboard sensing for whole-body humanoid evasion. A policy trained with training-time CBF guidance and a head-mounted camera only achieves near-oracle dodge performance, and zero-shot deployment on the Unitree G1 succeeds on 95% of real-world throws.

TacWAM：基于锚点引导与力学感知触觉预测的世界动作模型

TacWAM 是一个预测未来触觉状态（外观、密集力场与形变流）来监督接触丰富操作学习的世界动作模型，在四个真实世界任务上平均成功率达 75.0%，比最强基线高出 37.5 个百分点。

FasTac：面向高速高精度三维形貌与力感知的曲面多光谱视觉触觉传感器

FasTac 是一款曲面视觉触觉传感器，将多光谱光度立体、动态卷积力估计与 FPGA 硬件加速集成于紧凑形态。深度误差降至 0.0415mm MAE，三轴力估计 NMAE 约 2.4%-2.7%，FPGA 部署将处理延迟缩短至 1.09ms。

PAC-MAN：面向全身安全的人形机器人躲避球感知感知 CBF-RL 框架

PAC-MAN 将控制屏障函数（CBF）安全机制与贴近部署的真实机载感知相结合，用于人形机器人全身闪避。仅凭头戴相机与训练期 CBF 引导训练的策略即可达到接近特权状态oracle的闪避表现，零样本部署到宇树 G1 后在真实世界投掷中成功率达 95%。

SymmGrid: Super-Scaling On-Robot Learning with Parallelized Symmetries and Egocentric-Exocentric Visual Perception

SymmGrid uses parallelized symmetry transformations to dramatically accelerate on-robot reinforcement learning. Real-world manipulation tasks achieve 1.37-2.17× wall-clock training speedup, with convergence in as little as 10.9-16.6 minutes for peg-insertion and cable routing, and up to 2.59× improvement in normalized AUC.

HumanCLAW: Can Vision-Language Models Act Through a Body?

HumanCLAW introduces an evaluation framework that decouples action decision-making from motor execution for embodied AI, benchmarking 9 state-of-the-art VLMs across 1,218 egocentric find-navigate-interact episodes in 41 scenes. The best model achieves only 16.8% — revealing that current VLMs fundamentally lack embodied self-awareness.

TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM

TurboVLA reformulates the conventional V→L→A VLA paradigm into a direct V+L→A mapping, eliminating the LLM as the central bottleneck. With only 0.2B parameters, it achieves 97.7% average success on LIBERO at 32 Hz inference with just 0.9 GB VRAM on an RTX 4090, matching or outperforming much larger VLA policies.

SymmGrid：利用并行化对称性与第一人称-第三人称视觉感知加速机器人真实学习

SymmGrid 利用并行化对称变换大幅加速机器人真实强化学习。在真实机器人操作接触任务上实现 1.37-2.17 倍的训练加速（壁钟时间），插销插入和缆线布设在 10.9-16.6 分钟内收敛，归一化 AUC 提升最高达 2.59 倍。

HumanCLAW：视觉语言模型能否通过身体行动？

HumanCLAW 提出了一种将动作决策与运动执行解耦的具身 AI 评估框架，在 41 个场景中的 1,218 个第一人称“寻找-导航-交互”长程任务上评估了 9 个最先进的 VLM。最佳模型仅达 16.8%——揭示了当前 VLM 从根本上缺乏具身自我意识。

TurboVLA: 在RTX 4090上以32Hz、小于1GB显存运行的实时视觉-语言-动作模型

TurboVLA 将传统的 V→L→A 范式重构为直接的 V+L→A 映射，消除了大语言模型作为中心瓶颈。仅 0.2B 参数，在 LIBERO 上达到 97.7% 平均成功率，RTX 4090 上以 32Hz 推理，仅需 0.9GB 显存，匹配或超越远超其规模的 VLA 策略。

HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone

HiFi-UMI raises the fidelity of robot-free UMI data collection to 3 mm end-effector accuracy using head-mounted stereo-inertial SLAM, native relative pose estimation, and wide-angle stereo cameras. Policies post-trained solely on this data match in-domain teleoperation across three backbone families, and a 4000-hour pretraining corpus lowers action error on unseen tasks by 41%.

Pictura: Perspective-View Self-Play at Scale for Driving

Pictura is a GPU-accelerated multi-agent driving simulator providing egocentric perspective rendering at every step, enabling the first large-scale driving self-play policy trained directly from perspective images without privileged observations. The resulting Alberti policy approaches privileged vectorized performance and transfers zero-shot to Waymo layouts.

$\pi\mathbf{R}^2$: Reactive Real-time Flow Policies

πR² makes large-backbone action-chunking flow policies reactive and real-time by splitting conditioning into a fast proprioception channel (fresh every tick) and an asynchronously updated slow vision-language channel, combined with a latency-adaptive flow schedule. Applied to GR00T-N1.7, it replans at ~25Hz on an A5000, improving success by up to 30% in real-world tasks.

HiFi-UMI: 仅使用高保真UMI数据训练可部署的机器人操作策略

HiFi-UMI 通过头戴式立体惯性SLAM、原生相对位姿估计和超广角立体相机，将机器人无需遥操作干预的UMI数据采集精度提升至末端执行器3毫米。仅基于此数据微调的策略在三个骨干网络家族上与同场景遥操作基线性能持平，4000小时预训练语料将未见任务的动作误差降低41%。

Pictura: 基于第一视角的大规模自博弈自动驾驶策略学习

Pictura 是一个 GPU 加速的多智能体驾驶模拟器，在每个时间步渲染每个智能体的自我中心视角，首次实现了无需特权观测、直接基于第一视角图像的大规模驾驶自博弈策略训练。训练出的 Alberti 策略性能接近使用特权向量化观测的对应策略，并能零样本迁移至 Waymo 数据集。

$\pi\mathbf{R}^2$: 具有反应能力与实时性的流策略

πR² 通过将条件输入拆分为快速本体感知通道（每控制周期更新）和异步更新的慢速视觉语言通道，并结合延迟自适应流调度，使基于大骨干网络的动作分块流策略获得反应能力和实时性。应用于 GR00T-N1.7 后，在 A5000 GPU 上以约25Hz频率重规划，真实场景任务成功率最高提升30%。

Learning Reusable Hybrid Motion Priors for Humanoid Locomotion from Motion Imitation

A three-stage pipeline that turns motion-imitation skills into a reusable Hybrid Motion Prior (HMP) for humanoid locomotion. An expert policy learns to imitate retargeted human motion-capture clips, is distilled into a frozen RVQ codebook architecture, and task-level policies then solve locomotion tasks by selecting discrete codebook entries. The velocity-tracking policy is deployed on a real Unitree G1 robot.

FeelWorld: Visuo-Tactile World Model for Hierarchical Contact Prediction and Planning

A hierarchical visuo-tactile world model that jointly predicts future visual latents and three tactile states (contact state, 3D tactile latent encoding force info, slip state). Uses contact-gated asymmetric attention to maintain visual-only prediction before contact and enable joint visuo-tactile prediction during contact. Achieves LPIPS 61% lower than visual baseline after 80-step autoregressive rollout and 81.7% zero-shot planning success.

Data Pyramid for Embodied Manipulation

A systematic framework organizing the embodied data ecosystem into a five-tier pyramid: real-robot data, UMI-style data, egocentric and exocentric data, simulation data, and general vision-language data. Analyzes recent embodied foundation models through the lens of their data recipes, examining how different sources are selected, aligned, and mixed during pretraining.

Learning Reusable Hybrid Motion Priors for Humanoid Locomotion from Motion Imitation: 从运动模仿学习可复用的混合运动先验用于人形机器人 locomotion

提出三阶段流水线将运动模仿技能转化为可复用的混合运动先验（HMP）。先训练专家策略模仿人类动作捕捉数据，再蒸馏为冻结的RVQ码本架构，最后训练任务级策略通过选择离散码本条目实现 locomotion。速度追踪策略成功部署到真实Unitree G1机器人上。码本结构呈现可解释的层次化步态模式。

FeelWorld: Visuo-Tactile World Model for Hierarchical Contact Prediction and Planning: 视觉-触觉世界模型用于层次化接触预测与规划

提出层次化视觉-触觉世界模型FeelWorld，联合预测未来视觉潜在特征与三种触觉状态（接触、三维触觉潜在编码、滑动）。引入接触门控非对称注意力机制，在自由空间运动中保持纯视觉预测路径，接触后激活触觉预测。在芯片抓取、水果抓取和USB插入任务上，80步自回归滚动后LPIPS比纯视觉基线低61%，零样本规划成功率81.7%。

Data Pyramid for Embodied Manipulation: 具身操作的数据金字塔

本文系统性地梳理了具身智能体的数据生态系统，将其组织为五层数据金字塔：真实机器人数据、UMI风格数据、自我中心与外中心数据、仿真数据、通用视觉语言数据。围绕可扩展性与机器人对齐之间的张力展开分析，并从数据配方角度审视了近期具身基础模型的能力来源。

One Hand Watches The Other: Dynamic Multi-Agent Cooperation for Sample-Efficient Bimanual Manipulation in Dynamic Environments

DynaMAC resolves the causal limitation of multi-stream policies in dynamic settings by treating the opposite arm as a dynamic task parameter, enabling unified dynamic manipulation and bimanual coordination without an explicit leader-follower relationship. It outperforms leading baselines by 35+ percentage points while requiring 20× fewer samples, and generalizes zero-shot from static demonstrations to dynamic environments.

ViTacWorld: Scaling Visuo-Tactile World Models for Contact-Rich Robot Manipulation

ViTacWorld is the first visuo-tactile world model framework for contact-rich manipulation. It leverages public real tactile datasets and simulated environments to pretrain visuo-tactile-action trajectories, exploiting the fact that tactile signals exhibit a smaller sim-to-real gap than visual observations. It serves both as a data augmentation engine and a policy evaluation tool, generating physically meaningful rollouts that improve downstream tactile policies.

DynaMAC：面向动态环境的高样本效率双臂协作操作

DynaMAC 通过将对手臂视为动态任务参数，解决了多流策略在动态环境中的因果限制，为动态操作和双臂协调提供了统一表述，无需显式的主从关系。它在领先基线上提升 35+ 个百分点，同时所需样本减少 20 倍，并能从静态演示零样本泛化到动态环境。

ViTacWorld：面向接触丰富机器人操作的视触觉世界模型规模化

ViTacWorld 是首个面向接触丰富操作的视触觉世界模型框架。它利用公开真实触觉数据集和仿真环境预训练视触觉-动作轨迹，利用触觉信号比视觉观测具有更小的仿真-现实差距这一特性，同时作为数据增强引擎和策略评估工具，生成物理上有意义的轨迹以提升下游触觉策略性能。

RL-MACRO: A Cybernetic Closed-Loop Intelligence Framework for Multimodal Adaptive Robotic Craniotomy

RL-MACRO is a closed-loop cybernetic framework for autonomous robotic craniotomy that couples multimodal perception (force and sound), adaptive decision-making via offline Implicit Q-Learning (IQL), and robotic execution. A CNN-LSTM observer reconstructs the hidden temperature state from partial sensory feedback (R²=0.939, MAE=1.717°C). A dual-head Actor dynamically coordinates feed rate, spindle speed, and cutting depth. Experiments on bovine ribs and ex vivo goat skulls validate robust perception, adaptive recovery from force/temperature excursions, and smooth execution on irregular surfaces.

AXIS: A Growable Community-Driven Data Engine for Scalable Robot Manipulation

AXIS is a scalable community-driven data engine and benchmark for robot manipulation that enables browser-based teleoperation, automatically generates and validates new manipulation tasks, and transforms community-collected demonstrations into training-ready data. The AXIS dataset contains 207 diverse tasks and 50K+ trajectories. Continual pretraining on AXIS improves π0.5 by 5.8% overall, outperforms RoboCasa365 by 37.3%, and exhibits consistent scaling with increasing data volume.

Addressing the Orchestration Gap in Generalist Robots via Physical Agency

Pigey introduces a closed-loop physical agent orchestrator that decomposes goals into subgoals, commands low-level VLA policies, tracks outcomes, and recovers from failures — all without additional data or fine-tuning. It achieves 4x SOTA on LIBERO-PRO (12.8%→53.3%) and lifts frozen policies from near-zero to 90% on real-world reasoning-limited tasks, demonstrating the orchestration gap between motor skills and agentic reasoning.

RL-MACRO：面向多模态自适应机器人颅骨切除的控制论闭环智能框架

RL-MACRO是一个用于自主机器人颅骨切除的闭环控制论框架，整合了多模态感知（力和声音）、基于离线隐式Q学习（IQL）的自适应决策和机器人执行。CNN-LSTM观测器从部分感知反馈中重建隐藏温度状态（R²=0.939，MAE=1.717°C）。双头Actor动态协调进给速度、主轴转速和切割深度。在牛肋骨和离体山羊头骨上的实验验证了鲁棒的感知能力、对力/温度异常的适应恢复能力以及在不规则表面上的平滑执行。

AXIS：面向可扩展机器人操作的可生长社区驱动数据引擎

AXIS是一个可扩展的社区驱动机器人操作数据引擎和基准，支持基于浏览器的遥操作、自动生成并验证新操作任务、以及将社区收集的演示数据转化为训练就绪数据。AXIS数据集包含207个多样化任务和5万+条轨迹。在AXIS上持续预训练使π0.5的整体成功率提升5.8%，超越在RoboCasa365上预训练的模型37.3%，并随数据量增长呈现一致的扩展特性。

Pigey：通过物理智能体编排弥合通用机器人的编排鸿沟

Pigey 提出了一种闭环物理智能体编排器，能够将高层目标分解为子目标、调用低层 VLA 策略、跟踪执行结果并从失败中恢复——整个过程无需额外数据采集或微调。在 LIBERO-PRO 上达到 SOTA 的 4 倍（12.8%→53.3%），在真实机器人推理受限任务中将冻结策略从近乎零提升至 90% 的成功率。

Diffusion ReRoll: Revisable Denoising for Robotic Sequential Prediction

Diffusion ReRoll introduces structured re-noising for revisable denoising over prediction horizons, enabling earlier and later segments of a sequence to revise one another. It achieves relative gains of 21% over Diffusion Forcing in guided planning on OGBench, 56.5% relative improvement over Diffusion Policy on LIBERO-10, and best action-video consistency in unified video-action prediction.

HOST: Robots Acquire Manipulation Skills in Seconds from a Single Human Video

HOST enables a robot to acquire novel manipulation skills in an average of 29 seconds from a single human video, while retaining previously mastered skills. It achieves 62% average success rate across tasks — exceeding zero-shot baselines by 45% and even surpassing models fine-tuned on 50 robot demonstrations per task, requiring 50x fewer demonstrations and acquiring each skill 507x faster.

DEED: A Data-Efficient Post-Training and Experience-Driven Learning VLA Framework for Retail Humanoids

DEED is a systems-level approach to bridging the lab-to-store gap for humanoid VLA robots, validated on a supermarket chip-restocking task with a Unitree G1-Edu. It demonstrates that careful data design and targeted post-training with a single GPU can transform a policy that fails under naive fine-tuning into a competent real-world system, suggesting the lab-to-store gap is primarily a systems integration challenge.

Robostral Navigate: Monocular RGB-Only Visual Navigation with an 8B VLM

Robostral Navigate is an 8B vision-language model for visual navigation that consumes only monocular RGB images — the most ubiquitous sensor across robotic platforms — and predicts waypoints by pointing to the next target in the current camera view. Operating purely in image space makes it robust to changes in camera intrinsics and scene scale, enabling zero-recalibration deployment across wheeled, legged, and aerial robots. It sets a new state of the art on R2R-CE (77.4% success rate) and RxR-CE (75.1%), surpassing the best monocular methods by 10.5 points and even depth- or multi-camera systems by 5.3 points.

Diffusion ReRoll：面向机器人序列预测的可修正去噪方法

Diffusion ReRoll提出结构化重噪声方法，使预测序列中的早段和晚段能够相互修正。在OGBench上，它比Diffusion Forcing的引导规划成功率相对提高21%；在LIBERO-10上，比Diffusion Policy的动作预测相对改进56.5%；在统一视频-动作预测中取得了最佳的动作-视频一致性。

HOST：机器人从单段人类视频中秒级习得操控技能

HOST使机器人能够从单段人类视频中平均29秒习得新的操控技能，同时保留已掌握技能。它在各任务上达到62%的平均成功率——超过零样本基线45%，甚至超过每个任务用50段机器人演示微调的基线，且所需演示量少50倍，技能习得快507倍。

DEED：面向零售人形机器人的数据高效后训练与经验驱动学习VLA框架

DEED是一个弥合人形机器人VLA系统从实验室到商店部署鸿沟的系统级框架，在超市货架补货任务中基于Unitree G1-Edu和GR00T N1.6进行了验证。它表明，通过精心设计数据和使用单GPU进行定向后训练，可以将朴素微调下失败的策略转化为可在真实世界中运行的系统，证明实验室到商店的鸿沟主要是一个系统集成挑战。

Robostral Navigate：基于8B视觉语言模型的单目RGB纯视觉导航

Robostral Navigate是一个8B参数的视觉语言导航模型，仅需单目RGB图像——这是所有机器人平台上最普遍的传感器——即可通过在当前视野中指向下一个目标位置来预测路点。纯图像空间操作使其对相机内参和场景尺度变化具有自然鲁棒性，可在轮式、足式和空中机器人之间零校准部署。在R2R-CE上达到77.4%的成功率，在RxR-CE上达到75.1%，超越最佳单目方法10.5个百分点，甚至超过基于深度或多相机的系统5.3个百分点。

Eversion-Growing Robots Enable Safe Navigation and Endoscopic Imaging Within the Spinal Subarachnoid Space

This paper presents a 2 mm diameter eversion-growing robotic platform for low-friction navigation within the human spinal subarachnoid space. Validated through computational modeling, phantom experiments, and intact human cadaver studies, the robot achieved 65.2% reduction in mean interaction force and 48.0% reduction in peak interaction force compared to push-based insertion, with 150 mm of controlled intrathecal extension and no observable dural or neural damage.

Koopman DCM: Unstable Eigenfunctions as Data-Driven Representations for Legged Balancing

This paper generalizes Divergent Components of Motion as Koopman eigenfunctions, learning unstable eigenpairs from only 1 hour of real robot data. On a real biped, the learned Koopman DCMs improve reference walking pattern tracking and provide state-based viability constraints when combined with model predictive control.

Masked Visual Actions: A Pixel-Space Control Interface for Unified World Modeling

This paper introduces Masked Visual Actions, a pixel-space control interface that expresses robot actions as partially revealed trajectories of an arbitrary entity in a video. Finetuned with only 15 hours of masked examples, a single checkpoint achieves strong visual fidelity, forward dynamics prediction, inverse modeling, and model-based planning across diverse scenes and multiple embodiments.

基于外翻生长机器人的脊髓蛛网膜下腔安全介入与内镜成像

本文报道了一种直径2毫米的外翻生长机器人平台，能够在人体脊髓蛛网膜下腔实现低摩擦推进和转向。通过尸体实验验证，该机器人在150毫米鞘内延伸过程中，平均交互力降低65.2%，峰值力降低48.0%，且未对硬脊膜或神经结构造成可观察的宏观损伤。

Koopman DCM：基于不稳定本征函数的双足平衡数据驱动表示

本文首次将运动发散分量（DCM）推广为Koopman本征函数，利用仅1小时的真实机器人数据学习不稳定特征对。在真实双足机器人上，学习得到的Koopman DCM改善了参考步态跟踪性能，并与模型预测控制结合提供基于状态的生存性约束。

掩膜视觉动作：面向统一世界建模的像素空间控制接口

本文提出掩膜视觉动作（Masked Visual Actions），一种将机器人动作表示为视频中部分揭示轨迹的像素空间控制接口。仅用15小时掩膜示例微调，单个检查点即可在多样场景和多种机器人形态上实现高保真前向动力学预测和逆建模，并支持基于排名的模型预测规划和策略评估。

Manifold-Guided Motion Planning for Tight Assemblies

CMG-RRT proposes critical-manifold guided sampling-based planning, achieving 100% success on all tight assembly benchmarks including the first automatic solution of the Elk disentanglement puzzle, with a probabilistic completeness proof.

RynnBrain 1.1: Towards More Capable and Generalizable Embodied Foundation Model

RynnBrain 1.1 introduces an embodied foundation model family spanning 2B to 122B parameters with contact-point prediction and native 3D grounding. It outperforms all proprietary and open-source models on VSI-Bench, MMSI, and RefSpatial-Bench, and demonstrates cross-embodiment VLA deployment on Unitree G1, Astribot-S1, and Tianji-Wuji robots.

Importance Sampling and PCA for Finding Failures in Commercial Autonomous Vehicles

This paper applies Adaptive Stress Testing (AST) and Diffusion-based Failure Sampling (DiFS) to a commercial autonomous trucking stack, combined with PCA-based statistical analysis for failure mode classification. Both algorithms discover simulated collisions in merge and cut-in maneuvers that traditional Monte Carlo simulation cannot reach.

流形引导的精密装配运动规划

CMG-RRT 提出基于临界流形引导的采样规划方法，在精密装配任务中实现 100% 成功率，首次自动求解 Elk 益智拆卸谜题，并提供概率完备性证明。

RynnBrain 1.1：迈向更强大、更通用的具身基础模型

RynnBrain 1.1 发布涵盖 2B 至 122B 参数的具身基础模型家族，新增接触点预测与原生 3D 定位能力，在 VSI-Bench、MMSI、RefSpatial-Bench 等基准上超越所有开源及商业模型，并在 Unitree G1、Astribot-S1、天玑无极三款机器人上完成跨本体 VLA 部署验证。

重要性采样与主成分分析在商用车自动驾驶故障发现中的应用

本文将适应性压力测试（AST）和扩散故障采样（DiFS）应用于真实商用自动驾驶卡车栈，结合PCA分析对故障模式进行分类与诊断，在汇入和切入场景中发现了传统蒙特卡洛仿真无法触及的罕见碰撞故障。

Open-AoE: An Open Egocentric Manipulation Dataset and Toolchain for Embodied Learning

Open-AoE is a large-scale egocentric manipulation dataset with roughly 2,000 hours of real-world video, structured annotations, and an end-to-end toolchain designed to lower the data barrier for embodied AI research.

AC-VLA: Robust Out-of-Distribution Action Execution via Compositional Learning

AC-VLA is a plug-and-play compositional learning framework that addresses trajectory overfitting and perceptual shortcuts in VLA models, achieving ~28% absolute improvement on compositional OOD tasks while maintaining near-perfect in-distribution performance.

Orbis 2: A Hierarchical World Model for Driving

Orbis 2 introduces a hierarchical driving world model with a two-stage training paradigm combining diffusion forcing and teacher forcing, achieving SOTA results in long-horizon generation fidelity, steering responsiveness, and internal representation quality.

Handroid: Bridging Dexterous Hand and Humanoid

Handroid is a compact 27-DoF reconfigurable robot that functions as either a dexterous anthropomorphic hand or a 0.33m desktop humanoid, validated on manipulation, locomotion, and long-horizon reconfiguration tasks.

Open-AoE：面向具身学习的开源自我中心操作数据集与工具链

Open-AoE 是一个大规模的自我中心操作数据集，包含约2000小时的真实环境操作视频，提供完整的标注和工具链支持，降低具身智能研究的数据门槛。

AC-VLA：通过组合学习实现鲁棒的分布外动作执行

AC-VLA 是一个即插即用的组合学习框架，解决了 VLA 模型中的轨迹过拟合和感知捷径问题，在组合分布外任务上实现约 28% 的绝对提升，同时保持近乎完美的分布内性能。

Orbis 2：面向驾驶的分层世界模型

Orbis 2 提出了一种分层驾驶世界模型，采用扩散强迫预训练与教师强迫微调相结合的两阶段训练范式，在长时序生成质量、转向响应能力和内部表征质量上达到当前最优水平。

Handroid：灵巧手与人形机器人的桥梁

Handroid 是一款紧凑型 27 自由度可重构机器人，能够在一套硬件上分别充当灵巧的仿人手掌或 0.33 米高的桌面人形机器人，并在操作、运动规划与长时序重构任务上得到实验验证。

DriftWorld: Fast World Modeling through Drifting

DriftWorld replaces diffusion with drifting generative models for single-step world model inference at 30+ FPS — 17x faster than diffusion baselines — achieving state-of-the-art decision-making across multiple robotic manipulation benchmarks.

Scaling Behavior Foundation Model for Humanoid Robots

A systematic scaling recipe for Behavior Foundation Models in humanoid control, achieving 10-82% MPKPE reduction through motion tracking paradigm, strategic data synergy, and the Humanoid Transformer architecture.

Stigmergic Graph Memory: An Environment-Aware Approach for Many-to-Many Multi-Agent Pickup and Delivery

Stigmergic Graph Memory (SGM) records recent execution signals on warehouse nodes and edges to rank feasible endpoints and route preferences, achieving 20.5-36.7% throughput gains across 15 map-load conditions without altering collision constraints or planner validity.

RoboTTT: Context Scaling for Robot Policies — A New Scaling Axis for Foundation Models

NVIDIA and Stanford researchers propose RoboTTT, scaling visuomotor context to 8K timesteps — three orders of magnitude beyond prior work — enabling one-shot in-context imitation from human video, on-the-fly policy improvement, and full completion of a five-minute, ten-stage assembly task with 87% improvement over single-step baselines.

Reflex: Real-Time VLA Control through Streaming Inference

Reflex enables real-time streaming inference for flow-matching Vision-Language-Action (VLA) policies by exploiting the timestep-invariance property, achieving 2.58x speedup and 50Hz stable streaming on standard benchmarks.

BridgeFlow: Fast and Robust SE(2)-Equivariant Motion Planning with Flow Matching

BridgeFlow achieves fast SE(2)-equivariant motion planning via lightweight task-centric canonicalization paired with flow matching, delivering up to 15x inference speedup and 2x higher valid trajectory rates over diffusion baselines.

AeroAct: Action-Centered World-Action Models for Language-Conditioned Quadrotor Flight

AeroAct is the first world-action model (WAM) demonstrated on a real-world quadrotor, adapting a pretrained video diffusion Transformer to predict trajectory-action chunks from egocentric vision, proprioception, and natural language for closed-loop aerial navigation.

DriftWorld：漂移生成模型驱动的快速世界建模

DriftWorld利用漂移生成模型替代扩散模型，实现30+ FPS的单步世界模型推理，速度提升17倍，并在多个机器人操作基准上取得最先进决策性能。

扩展行为基础模型：迈向通用人形机器人控制

本文提出扩展行为基础模型（BFM）的系统性框架，通过运动跟踪学习范式、策略-数据协同和Humanoid Transformer架构，在仿真和真实场景中将人形机器人控制精度提升10%至82%。

Stigmergic Graph Memory：基于执行记忆的多对多多智能体货物搬运

本文提出Stigmergic Graph Memory (SGM)，一种有界衰退记忆层，通过记录仓库节点和边的近期执行信号来优化端点选择和路由偏好，在15种地图负载条件下实现20.5-36.7%的吞吐量提升。

RoboTTT：上下文长度缩放——机器人策略模型的新维度

NVIDIA联合斯坦福大学团队提出RoboTTT，首次将机器人视觉-语言-动作策略的上下文窗口扩展至8000时间步，实现从人类视频演示中一次性模仿学习、在线策略改进以及长时序复杂装配任务的完全自主完成，性能较单步基线提升87%。

Reflex：基于流式推理的实时VLA控制

Reflex通过利用时间步不变性（timestep-invariance）属性，实现了流匹配VLA策略的实时流式推理，在标准基准上达到2.58倍加速和50Hz稳定流式输出。

BridgeFlow：基于流匹配的快速鲁棒SE(2)等变运动规划

BridgeFlow通过轻量级任务中心规范化与流匹配相结合，实现了快速的SE(2)等变运动规划，在扩散基线方法上取得高达15倍推理加速和2倍有效轨迹率提升。

AeroAct：以动作为中心的世界动作模型用于语言条件四旋翼飞行

AeroAct是首个在实际四旋翼无人机上验证的世界动作模型（WAM），通过适配预训练的视频扩散Transformer，从第一视角视觉、本体感知和自然语言中预测轨迹-动作片段，实现闭环空中导航。

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