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版本更新 · v2arXiv:2610.30988

In-Context Learning World Models for Robot Dual-Arm Manipulation of Soft Garment with Partial Observations

Hengyi Sim, Quentin Rouxel, Dennis Hong, Darwin Caldwell, Fei Chen

原文摘要

Robust manipulation of soft objects, such as garments, is a critical skill for service and household robots to adopt, but remains challenging due to its high-dimensional state space, and material-dependent deformations. Humans address this challenge by using mental world models that "imagine" the shape and material properties from partial observations, which enables them to predict how actions affect future states. As such, embedding such world models in robots allows for better generalization in manipulating a wide variety of garments in the real world. However, learned world models typically require large amounts of real world data that is strenuous to collect, and a mechanism to infer latent properties that govern dynamics. We present a Soft-Object World Model that performs generative full-state estimation and action-conditioned dynamics prediction using a transformer backbone trained on large-scale simulated soft object interactions, with measures to mitigate the sim-to-real gap. By learning to infer latent shape and material cues through in-context learning over a variable-length partial point-cloud history, the model generalizes across robot embodiments, material parameters, garment types and shapes, and manipulation coordination modes without weight updates. The world model is then integrated with a Goal-Conditioned action planner for motion synthesis. We validate the approach in simulation and real world for bimanual garment folding, reporting improvements in reconstruction fidelity, prediction accuracy, and task success. Project Website: https://1am5hy.github.io/ICL soft object world model/

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.37509

Ego-CEVR: Learning Cross-Embodiment Visual Representations from Human Egocentric Videos

Keru Zhou, Zhirui Fang, Xinrun Xu, Yaopeng Lou, Zeyuan He, Zihao Zhu, Xinze Niu, Chen Wang 等 22 位作者

原文摘要

As egocentric video becomes increasingly available for embodied learning, an important research question is how to turn this low-cost, scalable source of data into robot manipulation capabilities. Egocentric videos naturally provide rich context for high-level task understanding, while real-robot demonstrations with wrist observations and action annotations provide supervision for fine-grained action alignment. We present Ego-CEVR, a data-native framework for hierarchical visual representation learning that assigns distinct learning objectives to global and interaction representations. The global module learns object relations, task state, and temporal progress through future prediction on egocentric video. The Interaction module interprets wrist observations in global context and learns local alignment and contact cues through robot action supervision. Separate residual paths expose both representations to the action policy, preserving direct access to task context alongside fine-grained execution information. Ego-CEVR supports independent pretraining of its global video module on large-scale human egocentric videos and videos from diverse robot embodiments, without requiring robot action labels or paired wrist observations.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38513

Force-Driven Hook Disassembly via Constraint-Aware Object-Centric Reinforcement Learning

Nadeen Elhadad, Takuya Kiyokawa, Kensuke Harada

原文摘要

The growing volume of end-of-life products has intensified the need for automated disassembly, yet hook-type connections remain challengin: their orientations vary, they deform under load, and their geometry is hidden, so vision alone cannot identify a feasible non-destructive disassembly direction. This work proposes a constraint-aware reinforcement learning framework that infers feasible extraction directions online from contact forces. The policy is trained in a robot-agnostic, object-centric simulation on a simplified peg-in-hole pair and deployed zero-shot on a physical robot, where it transfers to real connections with unseen, multi-stage constraint structures. Because instantaneous contact forces are noisy and locally ambiguous, the policy is coupled with a particle filter belief that provides the policy with temporally integrated directional information and reduces redundant exploratory motion. The approach is validated on four real-world tasks, from peg-in-hole pairs to the inner propeller and front cover of an end-of-life air-conditioning unit. The full method achieves success rates of 100%, 100%, 90%, and 75% with the highest path efficiency on every task, and raises the success rate on the air-conditioning cover from 45% to 75% relative to a policy trained without the belief. These results indicate that force-driven reinforcement learning can enable disassembly without product-specific models or demonstrations.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38508

SaiVLA-JEPA: Evaluating Future Latent Prediction in JEPA-Conditioned VLA Control

Rufeng Xiao, Ruihao Xiao, Wei Chen, Xinhai Sun

原文摘要

Joint-embedding predictive architectures (JEPAs) learn to predict representations rather than pixels, offering a compact way to model how visual scenes evolve. We ask whether this predictive objective improves closed-loop control when a vision-language-action (VLA) policy already uses V-JEPA features. We call our JEPA-conditioned flow policy family SaiVLA-JEPA. It combines a vision-language model with a diffusion transformer action expert and receives features from a frozen V-JEPA encoder. Two lightweight auxiliary heads predict future representations during training and are omitted at inference, leaving the action sampler unchanged. We compare policies without JEPA features, with current JEPA conditioning, and with additional future supervision on 10,030 paired LIBERO-Plus episodes. Current JEPA conditioning reaches about 82.5% success versus 71.6% without JEPA, while future supervision provides no detectable aggregate gain at the evaluated training budget. We separately evaluate SaiVLA-JEPA-TCA, which adapts latent context, and SaiVLA-JEPA-TPA, which adapts selected parameters. The context-retry gain is a system-level result under a larger attempt budget; parameter adaptation shows no consistent advantage over the frozen policy and target controls. These results motivate assessing latent-prediction objectives through paired closed-loop rollouts rather than latent loss alone.

cs.RO · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38502

OmniQuad: a Wheeled-Legged Hybrid Robot with Omnidirectional Wheels

Francesco Iotti, Alok Ranjan, Franco Angelini, Manolo Garabini

原文摘要

Combining wheeled and legged locomotion within a single robotic platform offers the potential to harness the advantages of both systems. The development of such hybrid systems remains an active area of research. While legged robots exhibit remarkable adaptability for traversing uneven and rugged terrains, their relatively low speed limits their practicality in indoor environments. Conversely, wheeled robots demonstrate higher efficiency on flat surfaces but often encounter difficulties when navigating obstacles such as steps. This paper introduces OmniQuad, a novel hybrid robot that integrates mecanum wheels with legged locomotion to exploit the benefits of both modalities. Mecanum wheels provide omnidirectional mobility, enhancing the robot's manoeuvrability by enabling agile movement in confined spaces and the ability to maintain a consistent orientation during task execution. In this work, we first present the design of a custom wheeled-foot featuring mecanum wheels. Subsequently, we describe the complete robot assembly, comprising these wheeled feet and EM-Act actuators. Finally, we detail the control scheme developed to govern the OmniQuad and evaluate its performance through experimental trials conducted in both indoor and outdoor environments.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.31302

Whole-Body Motion Guided Reference Generation for Uncertainty-Aware Compliant Assistance

Muyuan Ma, Long Cheng, Lijun Han, Haotian Zhai, Xinpan Meng, Xiuze Xia, Houcheng Li, Zeyu Liu

原文摘要

Exoskeletons have shown great potential for augmenting human physical capabilities, yet generating reliable assistance remains challenging because human motion patterns and interaction requirements vary substantially in practice. To address this problem, a dual-loop assistance strategy is proposed to couple multimodal exoskeleton trajectory generation with variable admittance control, where trajectory variance links the outer-loop generator and the inner-loop controller. The generator synthesizes future exoskeleton trajectories through denoising, primarily using whole-body human motion and exoskeleton proprioception, with task descriptions as auxiliary semantic cues. To reduce cross-modal representation mismatch, a three-stage training pipeline is introduced to progressively optimize the human motion encoder, the exoskeleton proprioception encoder, and the trajectory generator. In the inner loop, multiple future trajectories are sampled from the generator to estimate the trajectory mean and variance. The mean is used as the control reference, while the variance provides an uncertainty measure for prediction reliability. This measure is further incorporated into a stiffness regulation law, enabling compliant assistance with input-to-state stability under bounded lumped interaction input. Experimental results demonstrate that the proposed method significantly reduces muscle activation levels compared with standard baselines, while also decreasing users' metabolic energy expenditure relative to the unassisted condition.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.31116

NTR: Neural Token Reconstruction for Scene Token Bottleneck in End-to-End Driving

Jiahui Li, Jiawei Sun, Zixiang Ren, Ming Liu, Jiamin Shi, Ruiteng Zhao, Zhiyang Liu, Liying Liu 等 10 位作者

原文摘要

Recent perception-free end-to-end (E2E) autonomous driving methods bypass explicit perception outputs by compressing dense image patch tokens into compact scene tokens for downstream trajectory generation and scoring. While these scene tokens form a compact visual bottleneck for the planner, they receive supervision solely from the planning objective, providing limited constraints on the encoded visual information. To address this limitation, we introduce Neural Token Reconstruction (NTR), a representation learning framework to directly constrain the compact scene-token bottleneck in perception-free driving. NTR introduces a self-distillation masked latent reconstruction objective that reconstructs masked patch-level latent features using only compact scene tokens as reconstruction memory. This forces reconstruction gradients to pass exclusively through the scene-token bottleneck, encouraging scene tokens to preserve richer and less redundant visual representations for planning. We further introduce semantic priors derived from foundation-model annotations as a weak semantic interface biasing reconstruction targets toward driving-related structures without introducing explicit perception heads. All auxiliary reconstruction components are removed at inference time, leaving the deployed planner unchanged. NTR achieves state-of-the-art performance on three public autonomous driving benchmarks, including 8.0461 RFS on Waymo E2E and 94.1 PDMS / 90.9 EPDMS on NavSim1&2. The learned scene tokens exhibit lower pairwise redundancy and higher effective rank, indicating that effective bottleneck supervision improves both compact visual representation learning and planning performance.

cs.CV · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38443

Skeleton-Guided Geometry-Aware Auto-Labeling for Robotic Grasping: From 4-DoF Learning to 6-DoF Execution

Ali Sabzejou, Farbod Azimmohseni, Mehdi Tale Masouleh, Ahmad Kalhor

原文摘要

Robotic grasping relies on large manually annotated datasets, which are costly to create. We introduce a straight-skeleton-based algorithm for generating two-finger grasp keypoints and identifying regions suitable for stable, feasible grasps. Our automated pipeline produces consistent grasp annotations without human intervention. We also propose the Skeleton-based Generative Grasp Convolutional Neural Network (SkelGG-CNN), a 4-DoF architecture that learns geometric grasp features from skeleton-based guidance. To extend auto-labeling to 6-DoF grasping, we develop a generalized quasi-3D skeletonization algorithm that operates directly on point clouds. Using automatically generated 4-DoF labels, SkelGG-CNN achieves accuracies of 95.42%, 96.55%, and 97.1% on Jacquard V1, Jacquard V2, and Cornell, respectively. On the GraspNet novel split, 6-DoF auto-labeling achieves an average precision of 18.33, comparable to state-of-the-art model-based approaches. In simulation, the 6-DoF method achieves success rates of 98.4% on Dex-Net and 89.1% on EGAD!. Real-world experiments with a Delta parallel robot show success rates of 97.4% on single household objects and 95.0% in cluttered scenes, outperforming state-of-the-art methods without domain adaptation. The 4-DoF pipeline adapts across environments and produces annotations approaching human-level precision, enabling efficient training of lightweight models on small datasets. The generalized 6-DoF method provides scalable, geometry-aware annotations for cluttered 3D scenes.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.04527

Retrieve in Time, Correct in Frequency

Yuze Fan, Yue Cao, Pengjie Gao, Haojia Gao, Guangqiu Guo, Ziyue Zhang, Junbo Tan, Bokui Chen 等 10 位作者

原文摘要

Frozen vision-language-action (VLA) policies generate temporally extended action chunks, but long-horizon manipulation remains vulnerable to accumulated execution error and visual aliasing across task stages. Successful rollouts provide useful corrective evidence, yet current frame retrieval can return progress-misaligned actions,while direct replay or time-domain fusion can overwrite the reactive structure of the policy proposal. We introduce Retrieve in Time, Correct in Frequency (RTCF), a training-free test-time correction framework that improves frozen VLA performance with low model-side overhead.RTCF separates which experience to retrieve from which part of its action to transfer. Progressive Memory Alignment (PMA) causally aligns the growing visual execution history with complete successful trajectories through incrementally updated monotonic frontiers, jointly identifying a relevant memory and the current aligned memory position without stage labels. From the aligned action chunk,RTCF transfers a coefficient-wise-clipped low-frequency residual on motion channels. Higher-frequency components and gripper decisions remain inherited from the frozen policy. Across four LIBERO suites and 2,000 episodes per condition, RTCF raises aggregate success from 86.4% to 88.4% and improves LIBERO-Long from 61.6% to 68.6%.These gains require no parameter updates, repeated VLA inference, or additional GPU resources: correction can be performed on the client CPU after a single policy invocation, and the median latencies sum to only 10.99 ms per action chunk

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38405

SpaceAct: Benchmarking 3D Spatial and Temporal Reasoning via Closed-Loop Action and Active Perception

Chenglong Shi, Zekai Yuan, Yue Song, Jon Pu, Will Wu

原文摘要

To solve problems in 3D space rather than merely describe images, multimodal large language models (MLLMs) need 3D spatiotemporal reasoning: reconstructing 3D geometry from occluded 2D views, anticipating physical dynamics, and executing precise, closed-loop actions. Prior studies test passive VQA on static images or symbolic planning, requiring no execution or supplying privileged coordinates while ignoring collision, statics, and timing. We introduce SpaceAct, an interactive benchmark evaluating 3D spatiotemporal reasoning via closed-loop physical action and active perception, with SAPA, a Spatiotemporal Active-Probing Agent framework. Built on interlocking bricks and mechanical puzzles, it has 108 verified instances across 36 task families in six categories, without privileged coordinates, and diagnostic modes (+plan, +state, no-camera, blind, L7 levers). In brick-bridging or aperture-threading, models rotate the camera to resolve occlusion, read in-scene rulers, output 6-DoF poses under swept-collision and statics checks, and self-correct via split-screen rollback feedback (dynamic tasks freeze time during deliberation and give five strobe frames). Across 10 frontier MLLMs on the 44 hardest instances, scores span 4.8-79.1 (seven solve at most 27.3% of 6-DoF tasks); for the top three, a reference plan (+plan) closes 44% of the gap while leaving collisions at 5.2 per episode, whereas adding exact 3D state (+state) closes 90% and cuts collisions to 0.5; for four others, +plan lifts the mean score from 10.9 to 35.3 and +state to 81.8; and 46% of failures are collision or observation loops. SpaceAct and SAPA advance evaluation to closed-loop physical interaction, showing that even with a valid plan, MLLMs remain bottlenecked by 3D visual grounding and action refinement, while offering a testbed for active multi-view exploration and explicit 3D grounding.

cs.RO · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38400

When "No Worries" Went Wrong: HRI Lessons for Foundation-Model Robots

Eliahu Khalastchi

原文摘要

When a person declined our robot's invitation to play, Alice turned her shoulder away and said "No worries, it's all good." Only after a brief pause did she drive off. The words and gestures were plausible but their order changed the message. In another episode, Alice informed a host that his visitor had arrived---while the visitor stood right beside him. Both came from a three-year ROS 2 HRI consortium that integrated large language and vision models as they emerged. Today's foundation models give robots broader capabilities to perceive their surroundings, reason about situations and generate behavior. Yet capability alone does not ensure that a changed situation shapes the action that reaches a person. Evidence may never be acquired, or a purpose or constraint may fail to survive planning and execution. A system may even report Success when an action no longer serves its intended purpose. Drawing on the consortium's engineering experience, we develop five design lessons about attention, knowledge, commitments, embodied timing and action authority. We then introduce TRACE, a lightweight review that changes one interaction premise, follows it to a decision or enforcement boundary, and produces an actionable architecture finding. A bounded analysis of a published LLM-generated behavior-tree system illustrates how an available clarification mechanism can go unused. The engineering lesson is to preserve accountability boundaries, not the computational pipelines that once exposed them.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38388

ELEVATE: Agentic Controller Acquisition from Task Failure for Quadrupedal Manipulation

Shiqi Liu, Radu Corcodel, Ding Zhao

原文摘要

Quadrupedal manipulators can extend their workspace through whole-body interaction, but task-planning agents' capabilities remain limited by their available controllers. We present ELEVATE, a framework that uses persistent failures during task-program refinement to identify and acquire missing whole-body control capabilities. When these failures suggest a missing physical capability, a coding agent formulates and iteratively refines a reinforcement-learning problem, specifying behavior-specific rewards, curricula, and termination conditions. Validated controllers are added to the capability library and composed with existing controllers to retry the task. Starting from a single pretrained loco-manipulation controller, ELEVATE autonomously acquires controllers for wall-supported elevation, supported manipulation, and wall descent across a sequence of seven simulation tasks. These controllers are reused in subsequent reaching and object-manipulation tasks. On the five tasks beyond the initial controller's workspace, ELEVATE achieves mean success rates of 98.4%-100% across five independent runs, while coding-agent baselines with a fixed controller library fail to complete these tasks. The synthesized task programs, together with the acquired controllers, also transfer to a physical quadrupedal manipulator without real-robot training or fine-tuning, achieving 18/20 successes in rack retrieval and 16/20 in floor-to-rack placement. Videos and demonstrations are available at https://agentic-quad-elevate.github.io/public/.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.08285

Ego-OSCAR: Egocentric Open source Stereo CAptuRe System

Senthil Palanisamy, Satpal Singh Rathore, Pratyush Kumar Patnaik, Shubhanshu Khatana, Gunjan Paul, Abhishek Anand

原文摘要

Egocentric vision has scaled its corpora far faster than its instruments. Calibrated stereo rigidly coupled to a synchronized inertial stream is not new; research-grade headsets deliver it. What does not exist is an open version: those platforms are closed products a third party cannot build or extend, and the consumer cameras used elsewhere are monocular and rolling-shutter. The instrument is the least reproducible part of an otherwise open pipeline. We present our system, an open-hardware head-mounted capture platform, at a complete bill of materials under USD 200 in commodity parts and 3D prints. It pairs a hardware-synchronized global-shutter stereo camera with a 6-axis IMU, an embedded Linux SBC for on-device encoding, and a real-time microcontroller that bridges the camera and inertial clock domains to a residual of 700 microseconds and watchdogs the recording against silent failure. We release the full stack alongside the device: CAD, firmware, recording and time-synchronization tooling, and our corpus, roughly 550 hours of stereo video over 1,462 sessions and 25 contributors, every device shipping its own stereo calibration. The corpus ships with model-predicted annotation layers rather than raw video alone, including per-frame 3D hand reconstructions spanning essentially the whole timeline; these are automatic predictions, not human-verified ground truth, and we report them as such. We characterize the instrument along sensor fidelity, data utility, and six months of distributed field deployment. Our system is not the highest fidelity egocentric rig; it is the cheapest one a reader of this paper can reproduce, and the only corpus here that others can extend rather than only consume.

cs.CV · cs.AR · cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.32554

HGWM: Hierarchical Graph-Guided World Model for Zero-Shot Object Navigation via Graph Matching

I-Tak Ieong, Hao Tang

原文摘要

Zero-shot object navigation requires an agent to reason about where a target may be found while retaining evidence from previous observations. We present HGWM, a hierarchical graph-guided world model that combines a category-conditioned room--anchor prior with persistent spatial-semantic memory. A coarse stage uses a vision-language model to score candidate headings, while a fine stage explicitly compares observed objects and relations with the goal prior. Phase-dependent fusion integrates these signals with a language-model recommendation to guide local navigation. HGWM achieves 62.6\% success rate (SR) and 31.5\% success weighted by path length (SPL) on HM3D v0.1, improving over WMNav by 4.5 and 0.3 percentage points, respectively. On MP3D, it achieves 46.8\% SR and 17.9\% SPL, with gains of 1.4 and 0.7 points. On HM3D v0.2, the full model reaches 74.4\% SR and 31.9\% SPL; removing individual components reduces SR by 0.9--2.3 points and SPL by 0.6--1.5 points. These results support the use of relational priors and persistent graph memory for target-directed search.

cs.RO · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38357

CSO: Refining Robotic Policies via Skill Distribution Alignment and Skill-Grained Optimization

Zhiyuan Xiang, Xiang Deng, Wanjie Tao, Xu Chen, Zhao Jielun, Weili Guan

原文摘要

Discretizing continuous actions into discrete skills using methods like VQ-VAE has emerged as a powerful paradigm for robotic manipulation. However, quantization errors yield a suboptimal distribution for the skill prior, degrading its performance. While reinforcement learning offers a path for refinement, its direct application faces unstable encoder updates and a granularity dilemma in importance sampling. To address these challenges, we introduce Cascaded Skills Optimization (CSO), a two-stage post-training framework. First, Rejection-Sampling Supervised Fine-tuning aligns the model's observation-to-skill mapping with the distribution of successful online trajectories via supervised fine-tuning. Second, with the observation encoder frozen, Skills Policy Optimization computes an independent, clipped importance ratio for each complete skill, enabling stable and efficient updates. With a lightweight QueST-style policy, CSO raises overall LIBERO success from 80.1% to 97.5%, improves performance on RoboTwin2.0, and achieves 61.0% success across five physical single-arm tasks. Additional comparisons with FAST, discrete BEAST, and fixed-buffer IQL further validate the effectiveness of our two-stage post-training strategy.

cs.RO · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38351

Proprioceptive Finger Force Estimation for Reliable Box Manipulation

Junha Cha, Chanwoo Kim, Kisang Park, Yeoncheol Jang, Doyoung Kim, Seungmin Kim, Kyungjae Lee, Sungjoon Choi

原文摘要

Reliable box manipulation requires successful placement without damage, which depends on maintaining sufficient contact force without excessive loading. We hypothesize that per-finger force feedback improves manipulation reliability relative to open-loop finger commands. To this end, we present Finger External-Force Estimation from Proprioceptive SignaLs (FEEL), an uncertainty-aware residual method that estimates per-finger external force magnitude from proprioceptive histories. Our method combines a calibrated virtual-spring estimate with a residual distribution predicted by a Mixture Density Network (MDN) and requires no dedicated contact sensors during deployment. The estimated force is used for continuous per-finger adaptation, while predictive uncertainty is evaluated as an empirical signal of ambiguity in proprioceptive force estimation. Across ten fingers and contact forces spanning 0-20 N, our method achieves a mean RMSE of 0.251 N, reducing the error by 92.8% relative to the virtual spring and by 9.4% relative to an end-to-end MDN. In box pick-and-place, force adaptation maintains task success at 10/10 and increases both damage-free outcomes and reliability from 7/10 to 10/10. The project page is available at https://finger-force-estimation.pages.dev/

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.34982

ActionUNet: Improving Robustness of VLA Models with Efficient Multi-scale Fine-tuning

Di Zhu, Ziheng Yan, Fang Wan

原文摘要

Vision-Language-Action (VLA) models have shown great promise for robotic manipulation by mapping multi-modal semantics to physical actions. However, this mapping inherently struggles to align these coarse-grained semantics with fine-grained temporal execution. It leaves VLA models with limited generalization and insufficient robustness in cluttered environments. To overcome this issue, we propose ActionUNet, an efficient multi-scale fine-tuning framework that enhances pre-trained VLA models with minimal computational cost. ActionUNet first constructs a lightweight temporal U-Net within the temporal-aligned action feature space to fuse hierarchical structural priors, effectively bridging the scale gap between semantics and temporal executions. Recognizing that multi-scale modeling can disrupt microscopic temporal continuity and cause mechanical oscillations, ActionUNet then employs a conditional SIREN as a continuous action decoder. Equipped with explicit second-order smoothness constraints, this decoder guarantees temporal continuity and reduces high-frequency motion jitter. By smoothing temporal discontinuities from multi-scale fusion, this continuous formulation reduces mechanical execution failures while preserving the base VLA model's generalization and manipulation robustness. Extensive experiments on RoboTwin 2.0 and LIBERO-Plus benchmarks, together with real-world hard evaluations, demonstrate that ActionUNet significantly improves π0.5 success rates by absolute 9.8%, 6.1%, and 11.4%, respectively, while also generalizing to the regression-based OpenVLA-OFT backbone, highlighting its effectiveness and efficiency as a fine-tuning strategy. Code and implementation details are available at https://github.com/Di-Zhu123/ActionUNet.

cs.CV · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.11884

Mixture of Frames Policy: Multi-Frame Action Denoising for Bimanual Mobile Manipulation

Dian Wang, Jisang Park, Xiaomeng Xu, Han Zhang, Shuran Song, Jeannette Bohg

原文摘要

Robotic manipulation is inherently multi-frame: local actions may be simple in an end-effector frame, while transport, upright-object handling, and whole-body coordination are better represented in a base-aligned frame. However, modern diffusion-based visuomotor policies typically commit to a single predefined action frame, forcing one denoiser to model action distributions that are often unnecessarily complex in that frame. We propose Mixture of Frames Policy (MoF), a diffusion policy that performs synchronized action denoising across multiple coordinate frames. MoF maintains a single canonical diffusion state, re-expresses it in several task-relevant frames, applies frame-specialized denoisers, and fuses their noise predictions back in the canonical frame. To make this possible for intermediate noisy diffusion states, we adopt a column-based 6D rotation representation within an SE(3) action parameterization that supports exact, differentiable frame transformations without requiring noisy rotations to lie on the SO(3) manifold. Across nine simulated bimanual manipulation tasks, we show that the best action frame is task-dependent and that MoF improves over oracle frame selection and standard Mixture-of-Experts (MoE) baselines. We further evaluate MoF on two real-world bimanual mobile manipulation tasks, demonstrating that it outperforms all constituent single-frame baselines.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38291

Compositional Diffusion Planning with Chunk Selection and Reachability Measure

Chanyoung Park, Youngjoong Kim, Jaesik Park

原文摘要

Compositional diffusion planners have demonstrated remarkable success on long-horizon diffusion planning by sampling short trajectories, namely chunks, and composing them into a long trajectory. However, averaging overlapping chunks can induce the mode-averaging problem, resulting in discontinuous trajectories. Several works aim to address this problem, whereas a non-negligible portion of plans remains infeasible. In this work, we propose Selective Compositional Diffuser (SCD), handling this problem by measuring reachability. To this end, we present a self-supervised learning method for the reachability classifier. This is trained on offline data and generated chunks jointly to address potential distributional mismatch, while reducing the bias inherited from the generated data. Based on this, we construct a graph whose edge costs represent whether consecutive chunks are reachable, and traverse the graph to find effective yet efficient routes. Lastly, we generate bridging trajectories and refine the composed trajectory for smooth transitions. We evaluate our method on OGBench stitching tasks. It practically reduces incompatible mode compositions and the mode-averaging problem compared to the studied baselines. Across tasks, SCD improves success rates, reaching state-of-the-art results on HumanoidMaze-Large and Giant tasks.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38266

Recursive Self-Improvement in Physical AI: Mechanisms, Evidence, and Evaluation

Xiaofeng Han, Kaize Luo, Zenghuang Fu, Zichong Zhu, Zhe Feng, Changwei Wang, Rongtao Xu, Weiliang Meng 等 9 位作者

原文摘要

Embodied systems can update policies, world models, skills, and executable programs from deployment experience, allowing one interaction to benefit later tasks. When a system also revises and reuses its data-acquisition, feedback, or learning procedures, experience changes the process that produces capability, introducing recursive self-improvement. This paper surveys research in physical AI through the connection between persistent capability and its improvement process. We first explain the functional distinction between learner updates and improvers, then compare policy learning, imagined experience, memory and skill accumulation, and executable harnesses. These approaches connect to revisions of curricula, rewards, and research programs in different ways. Contact validity, recovery, retention, and resource costs determine whether the resulting gains accumulate during continued deployment. An L0-L3 rubric distinguishes task-local adaptation, persistent capability gain, inherited process revision, and verified improver gain. Representative benchmarks and within-study contrasts relate these distinctions to existing results. We further propose common-data and interactive comparisons that evaluate reference and revised improvers from shared learner starts, separating the use of experience from its acquisition. This perspective connects the diverse methods, physical operating conditions, and evaluation evidence, underpinning embodied RSI. A comprehensive list of studies in this survey is available at https://github.com/nkd-lkz/physical-ai-rsi-survey.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.14561

From Video to Vision: Learning Generalizable Humanoid Carrying from Human Motion

Jianming Lu, Ziluo Ding, Zhen Zhao, Shuyang Jin, Han Wen, Xinrun Xu, Zhiyuan Xu

原文摘要

Most existing approaches rely on video or motion-capture data for reinforcement learning (RL) training; however, all of these methods require an upper-level module to generate reference trajectories in real time. In object carrying tasks, the humanoid can only lift objects from designated locations along the reference trajectory, resulting in poor spatial generalization. Moreover, existing policies can only be evaluated inside a motion-capture studio. To address these limitations, we propose \textbf{MOVER}, a multi-stage, Teacher-Assistant-Student, RL training framework for humanoid object carrying. We first collect data from videos and employ GENMO and GMR to retarget the video data, combining this with data augmentation techniques to generalize the data across objects of varying shapes and sizes. In the \textbf{first stage}, building upon the DeepMimic \cite{peng2018deepmimic} motion-training framework, we train the humanoid to transport objects from specified locations along designated trajectories. In the \textbf{second stage}, we relax the tracking of the reference motion and adjust the reward so that the humanoid learns to transport objects from different positions. In the \textbf{third stage}, we apply a Dagger-PPO distillation method to distill away the proprioceptive position observations and reference joint position of the humanoid. This allows the deployed policy to operate without relying on global position information from a motion-capture system; instead, the humanoid can directly perceive the object's pose from its onboard RGB-D camera to complete the carrying task. We design a series of simulation experiments to compare the success rates across stages (Stages 1 vs. 2) and before versus after distillation. We further transfer the policy to a real humanoid, successfully transporting objects at varying positions in a motion-capture-free setting.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38248

MATCH: Execution-Aware Hierarchical Reinforcement Learning for Vision-and-Language Navigation in Continuous Environment

Shuhao Ye, Yufei Wei, Demao Wang, Shichao Zhai, Rong Xiong, Yue Wang

原文摘要

Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires instruction-consistent planning and reliable execution under collision constraints. Yet many hierarchical approaches optimize the planner while keeping execution fixed. We introduce MATCH, a framework that jointly learns a language-conditioned planner and a goal-conditioned primitive-action executor. An execution-aware topological graph represents start-dependent execution options and integrates executor value estimates with realized outcome memory, enabling the planner to select execution requests and refine their targets through bounded continuous offsets. A transition-aware dense macro reward balances ordered progress along the reference route against execution residual, encouraging executable requests without substituting local success for instruction following. A staged curriculum culminates in synchronized on-policy updates, with the executor learning on planner-generated requests and the planner adapting its requests to the executor's current behavior. With 0.5B parameters, MATCH achieves state-of-the-art performance on the R2R-CE and RxR-CE benchmarks. Preliminary real-robot deployments further demonstrate its practical potential.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38234

SimRetract: Zero-Shot Sim-to-Real Robotic Assistance for Retractor Take-Over in Open Surgery

Xucheng Wang, Zhizhou Yang, Sung Eun Kim, Romain Hardy, Michael P. Sampson, Aakash Reddy, Ankit Pal, Xiaoman Zhang 等 10 位作者

原文摘要

We demonstrate the first zero-shot sim-to-real transfer for a surgeon-robot collaboration task in open surgery: retractor take-over. A robot arm takes a retractor from the surgeon's hand and preserves retraction at handoff, using a policy trained only on simulated demonstrations, without human demonstrations or real-world fine-tuning. Imitation learning from real demonstrations is limited by collection and labeling costs, and by performance degradation when the environment changes. Our framework, SimRetract, generates simulated demonstrations designed for transfer: the policy observes masked instrument images and grasp geometry, learns to recover from off-nominal states, and acts with margin for execution drift. Deployed zero-shot, it grasps the retractor in 90% of 50 surgeon-defined phantom presentations and preserves retraction at handoff in 78%. Results on unseen retractors suggest potential for generalization to instruments with similar grasp geometry. Across ten policy configurations and 1,200 simulated and real trials, we localize the sim-to-real gap to components: in simulation, grasp rates across ablations span 80-96%, while on the real robot removing the instrument mask alone drops performance to 22% grasped and 12% held. Transfer-oriented design thus makes zero-shot sim-to-real transfer feasible for a surgical assistance task without human demonstrations. Code is available here: https://anonymous.4open.science/r/simretract-58D2.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38233

HERON: Real-Time Obstacle-Aware Reachability Maps for Mobile Manipulators

G. Dupoiron, F. Michaud, J. P. Roberge

原文摘要

Mobile manipulator robots rarely reposition their base during manipulation, because doing so requires real-time knowledge of how well the arm can reach its surroundings among obstacles. Reachability maps (RMs) encode this knowledge, but existing methods are slow, coarse (typically 5 cm), or ignore real obstacles. This article asks whether a neural network can predict an obstacle-aware RM at 2 cm resolution from a live depth camera, on embedded hardware, fast enough for online use. We present HERON, a 3D U-Net trained on RMs computed with the GPU-accelerated inverse kinematics of cuRobo, first on synthetic scenes and then on real depth data, and deployed on a Jetson AGX Orin with TensorRT and INT8 quantization. HERON predicts the RM of a 2.56 m cube around the arm in 115 ms on average, for a measured system rate of 8.57 Hz, close to the 10 Hz update rate of the obstacle map, whereas computing the same map with cuRobo takes about 90 min. Against the cuRobo ground truth, 95% of the voxels lie within 1 class out of 51 with the FP32 model and within 3 classes with the INT8 model, with a Pearson correlation of 0.99 and a quadratic weighted kappa above 0.9. Accuracy degrades mainly at the workspace boundaries.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38221

CuraEval: Evaluating Demonstration Curation on Mixed-Skill Robot Data

Jiuyi Xu, Meida Chen, Yang Ye, Yangming Shi

原文摘要

Robot imitation learning uses demonstrations from operators of varying skill, but selecting training data remains challenging. Existing methods score demonstrations using smoothness, diversity, or estimated influence on policy performance, but differences in selection and evaluation protocols make it difficult to determine whether they outperform simple baselines. We introduce \textbf{CuraEval}, a framework combining mathematical analysis of demonstration scores with controlled comparisons against fixed size-matched random subsets and full-data training on mixed-skill robomimic data using Diffusion Policy and BC-RNN. We show that the evaluated PSD score depends only on episode length and squared position magnitudes, and produces nearly the same ranking as episode length on \textit{square} ($ρ=0.999$). With Diffusion Policy, selecting the shortest 50\% of demonstrations improves \textit{square} success from 70.7\% under random selection to 76.5\%. Compared with random selection, our adapted geometric-entropy LOO selector reduces success at 25\% retention from 94.7\% to 74.0\% on \textit{can} and achieves 41.7\% on \textit{square}, below the mean success rates of all three independently sampled random subsets (59.8--62.5\%). On the same 3 \textit{square} Diffusion Policy training seeds, length-based selection at 75\% retention improves success over full-data training from 76.7\% to 86.7\% under the original protocol, but lowers it from 80.5\% to 79.5\% after checkpoint reporting, rollout count, and evaluation seed change jointly. No evaluated curated condition significantly outperforms full-data training after Holm correction, although confidence intervals still permit useful gains. Code is available at https://github.com/jiuyixu25/CuraEval.

cs.RO · cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2606.30097

CylindTrack: Depth-Aware Cylindrical Motion Modeling for Panoramic Multi-Object Tracking in Mobile Robots

Buyin Deng, Kai Luo, Lingxin Huang, Xinqi Liu, Fei Cheng, Hang Zheng, Liming Yin, Kailun Yang

原文摘要

Maintaining consistent target identities and observation histories is essential for mobile robot perception. Panoramic Multi-Object Tracking (MOT) benefits from broad visual coverage but must address coordinate discontinuities at the equirectangular image seam and fluctuations in relative-depth estimates. Such fluctuations can undermine depth-aware association, highlighting the need for temporally consistent depth cues. We propose CylindTrack, a depth-aware cylindrical tracking-by-detection framework that jointly models panoramic topology and temporal depth consistency. Depth-Temporal Trajectory Modeling (DTM) maintains instance depth and its rate of change as filtered trajectory states. Spherical Spatio-Temporal Consistency Learning (SSTC) combines a Temporal Mixer with Spherical Geometry-aware Attention to refine depth-aware representations. A Topology-Aware Cylindrical Motion Model (TCMM) represents horizontal motion in an unwrapped angular state and incorporates periodic overlap, angular consistency, and filtered depth cues into association. Experiments on QuadTrack and JRDB demonstrate improved identity consistency and trajectory continuity, with HOTA scores of 33.67 and 31.12 and IDF1 scores of 40.45 and 34.33, respectively. The framework processes 28.56 and 21.34 frames per second, respectively, on an NVIDIA GeForce RTX 3090 GPU under the reported evaluation settings. These results support persistent panoramic multi-object tracking for mobile robot perception. The source code will be released at https://github.com/warriordby/CylindTrack.

cs.CV · cs.RO · eess.IV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38199

Embodied at Sea: Imitation Learning for Multi-Task Underwater Manipulation

Greta Perez-Haiek, Luca Macesanu, Kevin Angers, Mohammed Imam, Timothy Gambin, Christopher Clark

原文摘要

Behavior Cloning has demonstrated promising generalization capabilities for robotic manipulation in terrestrial environments. However, marine visual distortions and dynamic currents create a severe domain shift that remains a key bottleneck in adapting generalist architectures to underwater field manipulation. To address this, 1616 episodes of underwater manipulation data were collected in calm and turbulent waters and were used to train 14 generalist Vision-Language-Action (VLA) models (DynamicVLA) with varying proprioception observation configurations. Across three tasks on 1260 gradable on-hardware rollouts in a pool, 10 models outperformed the baseline, with the highest-performing model achieving a 93.3% overall success rate, including partial successes. Results are also introduced from a case study evaluating the performance of a single-task Action Chunking Transformer (ACT) and a generalist VLA (SmolVLA) policy for two tasks in marine archaeology and conservation: grasping and mooring. Experiments are performed on an ROV on and around the WWII wreck Coralita X127 and at Manoel Island Marina, Malta. Across 748 collected episodes and 508 gradable on-hardware rollouts, the ACT policy substantially outperformed SmolVLA (47.9% vs. 7.9% pooled success rate), earning a 66.7% success rate on the most successful task at the marine archaeological site. To enable further contributions to embodied intelligence in marine field robotics, the BLUEMBODIED KIT used to collect data, train models, and deploy policies on the ROV has been open sourced.

cs.RO · cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2610.30876

LocoManiLab: A Unified Testbed for Data, Policies, Controllers, and Deployment in Humanoid Loco-Manipulation

Dewei Wang, Jinrui Han, Jiyuan Shi, Chenyun Zhang, Xianbo Lu, Jiabao Wu, Jiqin Feng, Jipeng Kong 等 14 位作者

原文摘要

Humanoid robots must coordinate locomotion and manipulation in everyday environments, yet progress is difficult to assess across differing policies, controllers, and task settings. LocoManiLab provides an integrated testbed spanning data collection and generation, policy training, evaluation, and physical deployment. Its task suite covers 234 atomic and compositional tasks across five household scene categories, supported by 51.5 hours of simulated teleoperation. The evaluation covers twelve policies with a shared controller across task and scene splits and perturbation levels, two policies across five controllers, and studies of demonstration scale and data-source mixtures. Success rates fall on unseen tasks and scenes and generally decline as visual perturbations increase, with differences in generalization and robustness across policies. For data generation, a humanoid motion generation model is fine-tuned for loco-manipulation to study the alignment of its outputs with task execution requirements and its integration into the data generation pipeline. Three simulated loco-manipulation tasks closely match real-world setups, enabling evaluation of three policies in both domains. Similar policy rankings support the reliability of simulation-based evaluation.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.34051

BEHAVIOR-Spatial Benchmark: Evaluating Spatial Understanding for Robotics

Sirui Chen, Huang Huang, Qineng Wang, Wenlong Huang, Manling Li, Ruohan Zhang, Li Fei-Fei, Jiajun Wu

原文摘要

Robot tasks require grounding spatial language in physical scenes, but task success alone cannot establish whether spatial instructions are followed. We present BEHAVIOR-Spatial, a benchmark built on BEHAVIOR-1K that measures spatial question answering, point-based target grounding, and robot action to examine how VLM spatial understanding relates to VLA behavior. It comprises 3,256 spatial questions across 49 activities, 1,240 grounding queries across 14 manipulation tasks, and action evaluations covering those 14 tasks and 10 navigation tasks. Our manipulation protocol evaluates held-out configurations and instruction following when the requested target changes within a fixed training layout. The baseline achieves 41.6% on held-out configurations but only 16.1% under changed instructions. VLM adaptation and robot-data pretraining provide limited improvements, whereas oracle visual target cues raise these rates to 60.3% and 38.0%, respectively. Perception evaluations reveal policy-dependent correlations between VLM question-answering and grounding accuracy and VLA manipulation performance. Together, these findings motivate evaluating spatial instruction following alongside task completion and examining when spatial perception performance translates into robot behavior. Project website: https://arielchen07.github.io/behavior-spatial/.

cs.RO · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.36681

From Biological Curves to Soft Modular Robots: Learning-Based Inverse Design with Uncertainty-Guided Refinement

Dixiang Wang, Minghao Zha, Arifur Rahman, Yinhong Qin, Yitao Jiang, Shiyang Lu, Zebing Mao, Muhao Chen 等 9 位作者

原文摘要

Animals exhibit diverse shape-changing behaviors, from the undulatory swimming of eels to the arching movements of inchworms. Reproducing these biological shapes with modular soft robots offers a promising approach to developing versatile bioinspired robotic systems. However, translating a desired shape into a feasible robotic configuration remains challenging. Existing cable-driven modular soft robots rely on computationally expensive physics-based modeling and combinatorial search for inverse design, making it difficult to efficiently reproduce diverse target shapes. To address this challenge, we present a learning-based real-to-sim-to-real framework that translates biological curves into robotic configurations through surrogate-assisted inverse design and uncertainty-guided refinement. The proposed method combines learned inverse prediction with targeted candidate exploration, using a fast forward surrogate to evaluate and select suitable designs. The learned forward surrogate reduces deformation prediction time from $1.53 \times 10^5$ ms to 0.0008 ms, achieving approximately a $1.9 \times 10^8$-fold speedup. At the same time, the proposed inverse design method reduces design time from $1.99 \times 10^6$ ms to 4.18 ms. On 5,000 test samples generated from previously unseen threading patterns, the method achieves a 93% shape reconstruction success rate under surrogate-based evaluation. Physical demonstrations with four bioinspired robots further illustrate the translation of biological curves into realizable robotic shapes.

cs.RO · cs.LG

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arXiv ↗PDF ↗