行业研究动态

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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