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Featured Research

Last 7 days · Technical and industry impact
Featured paper Impact 89/100

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.

Traversal is the precondition for humanoids to leave the lab, and PASSAGE moves the scaling bottleneck away from writing task-specific reinforcement-learning objectives or curating motion libraries and onto collecting more scene-aligned human motion, which is a data-side path that can actually scale. One hundred hours of motion across 1,500 scenes, an explicit 6-to-100-hour scaling curve, and a complete stack that runs entirely onboard on a Jetson AGX Orin and was validated across 50 unseen physical layouts put it clearly ahead of the rest of the batch on completeness, empirical grounding and industrial relevance.
Featured paper Impact 88/100

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.

Tactile sensing is the most information-dense signal still largely absent from large pretrained robot models, and this paper shows it can be folded into an existing video prior rather than requiring a from-scratch tactile model. The design decisions are specific and defensible: per-fingertip encoding preserves finger identity, a finger- and pose-aware compressor keeps contact structure under compression, and the tactile latent is injected into the world model so contact evolution is predicted rather than merely conditioned on. The ablation is the strongest evidence, since removing tactile world modeling with tactile features and the action expert held fixed drops the four-task mean from 74.7 to 26.6, which separates the contribution of predicting contact from the contribution of sensing it. Efficiency numbers (100 demonstrations per task, 2.26x faster training, 1.29x faster inference, 0.5 dB visual-retention cost) make the approach plausible for teams without large tactile datasets.
Featured paper Impact 88/100

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.

This work moves the safety certificate from runtime supervision to a training-time teacher and then distills it into the policy, so deployment needs no online safety filter; that path speaks directly to the central tension between agility and safety on humanoid platforms. Its technical core is a transferable insight: putting the state-action certificate in the task policy control space rather than the raw joint space is what makes synthesis tractable at full body dimension. The controlled result is the strongest evidence, since holding the safety specification fixed and changing only sampling concentration moves the policy from crouching to a backward limbo maneuver, which shows that how the boundary of recoverability is explored determines which recovery the policy learns. A 29-DoF platform with hardware transfer is an unusually complete loop for safety-oriented reinforcement learning.

Latest Papers

ε4P: Upcycling Imperfect Data for High-Precision Robotic Manipulation

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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.

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

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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.

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

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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.

PredActor: Predictive Action Diffusion for Steerable Onboard Humanoid Control

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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.

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

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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.

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

industrial-arm

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.

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

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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.

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

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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.

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

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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%.

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

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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.

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

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

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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.

ModAR: Modality-Autoregressive World-Action Models

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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.

XPACE: Joint World and Action Modeling from Heterogeneous Experience

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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.

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

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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.

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

industrial-arm

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

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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

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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.

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

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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.

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

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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.

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

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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.

Show-Harness: Just a VLM Agent Can Play Robots

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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.

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

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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.

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

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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.

Learning Terrain-Adaptive Humanoid Locomotion on Granular Terrain

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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.

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

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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.

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

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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%.

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

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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.

Proxy Policy Steering

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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.

Ostrich: Taking Large Strides Through Stiff Contact in Differentiable Dynamics

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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.

Collision Snapshot Guided Time-Reversed Safety-Critical Scenario Generation

amr

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.

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

industrial-arm

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

humanoid

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.

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

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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.

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

software

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.

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

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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.

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

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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.

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

general

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.

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

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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.

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

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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.

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

amr

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.

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

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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.

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

general

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.

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

humanoid

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.

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

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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.

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

humanoid

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.

Spatially Aware World Action Model via Geometric Latent Diffusion

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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.

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

industrial-arm

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.

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

humanoid

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.

Non-Prehensile Throwing: A Reinforcement Learning Perspective

industrial-arm

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.

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

general

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.

Driving on Memory

amr

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.

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

amr

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.

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

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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: An Embodied World Action Model for Physical AI

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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.

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

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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%.

HumanTracker: Towards Comprehensive and Human-Aligned Motion Tracking Benchmark

humanoid

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: Action-Conditioned Video World Model for Robotic Manipulation

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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.

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

general

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.

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

humanoid

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

humanoid

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.

MiDAS: Adaptation of Generalist Robot Policies with Minimal Data

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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.

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

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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.

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

humanoid

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

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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.

RynnValue: Scaling Robotic Value Foundation Models with Temporal Distance

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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.

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

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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.

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

amr

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.

Learning Fault-Tolerant Locomotion with Adaptive Gait Timing

humanoid

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.

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

components

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.

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

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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.

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

humanoid

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.

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

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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.

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

general

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.

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

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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.

DreamWAM: Beyond RGB Future Prediction for World Action Models

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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.

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

amr

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.

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

industrial-arm

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

software

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

software

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.

Ego2Robot: Scalable Robot Data Synthesis from Egocentric Human Data

software

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.

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

general

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.

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

humanoid

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.

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

software

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.

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

software

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.

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

humanoid

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.

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

humanoid

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.

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

components

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.

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

software

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.

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

software

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.

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

general

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.

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

software

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.

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

software

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.

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

amr

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.

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

software

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%.

Data Pyramid for Embodied Manipulation

general

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.

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

general

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.

Learning Reusable Hybrid Motion Priors for Humanoid Locomotion from Motion Imitation

humanoid

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.

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

general

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.

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

industrial-arm

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.

Addressing the Orchestration Gap in Generalist Robots via Physical Agency

general

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.

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

general

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.

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

surgical

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.

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

amr

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.

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

humanoid

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.

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

general

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.

Diffusion ReRoll: Revisable Denoising for Robotic Sequential Prediction

general

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.

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

general

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.

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

surgical

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

humanoid

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.

Importance Sampling and PCA for Finding Failures in Commercial Autonomous Vehicles

amr

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.

RynnBrain 1.1: Towards More Capable and Generalizable Embodied Foundation Model

software

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.

Manifold-Guided Motion Planning for Tight Assemblies

industrial-arm

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.

Handroid: Bridging Dexterous Hand and Humanoid

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.

Orbis 2: A Hierarchical World Model for Driving

software

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.

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

software

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.

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

general

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.

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

amr

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.

Scaling Behavior Foundation Model for Humanoid Robots

humanoid

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.

DriftWorld: Fast World Modeling through Drifting

software

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.

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

drone

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.

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

general

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.

Reflex: Real-Time VLA Control through Streaming Inference

software

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.

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

humanoid

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.