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Noam Rotstein, Yoad Tewel, Chen Tessler, Haggai Maron, Gal Chechik and others · 2026-10-20
Video generation models can now depict remarkably realistic motion. Yet it is unclear how to use this motion beyond pixels: to animate a specific 3D asset and replay the animation in its 3D scene. For instance, a video model can show a monkey jumping over a crossbar, but it canno…
Yuxuan Kuang, Jiashun Wang, Yixuan Sun, Yufei Ye, Sungjae Park and others · 2026-10-20
Dexterous multi-finger hands hold promise to unlock diverse skills like functional grasps, in-hand reorientation, and dynamic handover that the human-designed world assumes, yet existing learning recipes force a trade-off. Goal-reaching reinforcement learning generalizes to arbit…
Rahul Sajnani, Diego Romeres, Mingyuan Zhang, Kai Gao, Carl Qi and others · 2026-10-20
Motivated by the human ability to integrate experience across language, perception, and action, we introduce UniWALM, a unified world-action-language model that learns video, action, and language in a single shared representation, where each modality is both a conditioning signal…
Jad Yammine, Malek Wahidi, Abrar Anwar, Yan Chang, Mihir Acharya and others · 2026-10-20
Warehouse robot fleets continuously observe operational conditions, but preserving these observations as reusable semantic memory incurs substantial computation, communication, and storage costs. For example, explaining recurring aisle blockages can require reviewing video from m…
Michael Chen, Hong Chul Nam, Xi Deng, Anima Anandkumar · 2026-10-20
Planning under uncertain control execution noise requires reliable feedback policies across diverse environments. We introduce Planning Under Noise (PUN), a neural operator that learns noise-aware value fields for planning in unseen geometries. Building on an established screened…
Zebin Xing, Yuhang Zheng, Yupeng Zheng, Yichen Zhang, Zichen Li and others · 2026-10-20
Evaluating and post-training robot policies through physical interaction is costly and difficult to scale, motivating action-conditioned world models as interactive simulators. However, visually plausible predictions can misrepresent contact dynamics, providing unreliable feedbac…
Xinxin Liu, Bingxin Xu, Wenrui Bao, Ming Li, Wentao Bao and others · 2026-10-20
Agentic robotics systems combine planners with robot harnesses to perform complex manipulation tasks. Recent work improves these systems through better harnesses and external memory, but relies on powerful closed-source models as planners and does not improve the planners' own ca…
Rundong Luo, Yunong Liu, Di Cao, Matthew Tancik, Shyamal Buch and others · 2026-10-20
Simulation is a powerful tool for robotics, but building diverse, simulation-ready scenes at scale remains difficult. Reconstructing scenes from single images is a promising alternative, but visual similarity alone is insufficient: objects must also be recovered, accurately place…
Kelvin Aladum, Nisar Ahmed · 2026-10-20
Robots exploring unknown indoor environments must decide where to move while much of the environment remains unseen. Predicting the occupancy of unseen space beyond the current sensor range can improve these decisions, but recent approaches commonly rely on learned models that re…
Om Shivam Verma, Shaya Farahmand, Lingjun Zhao, Lu Gan · 2026-10-20
Collaborative perception enables agents to retrieve the scene semantics and geometry beyond the field of view of any individual agent. This broader scene understanding enhances transportation safety and improves efficiency in multi-robot systems. However, existing collaborative s…
Yuto Shibata, Kotaro Amaya, Kashu Yamazaki, Lalit Jayanti, Yoshimitsu Aoki and others · 2026-10-20
Humanoid-human interaction requires robots to operate under severe partial observability: during close physical contact, the human partner is occluded, truncated, or outside the robot's field of view, causing pose estimation to fail and often necessitating external tracking or pr…
Qiayuan Liao, Yiyang Shao, Junfeng Long, Kevin Zakka, Roberto Horowitz and others · 2026-10-20
Tracking large human-motion datasets has become the dominant route to general, expressive humanoid whole-body control, but scaling across many motions is expensive. Current pipelines use model-free reinforcement learning, either merging per-motion specialists through lossy distil…
Pengying Wu, Mingye Jiang, Yunze Hu, Zhejia Wen, Kangjie Zhou and others · 2026-10-20
Object-goal navigation (ObjectNav) requires an embodied agent to find an instance of a specified category in an unseen environment and stop near it. The agent typically relies on an object detector but must judge online from a limited egocentric video stream, sometimes a single f…
Daniel Marew, Sangjun Park, Nisal Perera, Donghyun Kim · 2026-10-20
While large-scale human motion capture datasets provide a rich source of demonstrations for humanoid robot learning, retargeting the diverse motions to different humanoid robots remains a nontrivial challenge. Existing kinodynamic trajectory optimization pipelines are computation…
Raúl Castilla-Arquillo, Miguel Olivares-Mendez · 2026-10-20
Lunar surface exploration increasingly relies on autonomous rovers that must perceive and traverse unstructured terrain under extreme illumination conditions. Near the lunar poles, low sun elevations produce long, high-contrast shadows that challenge stereo vision, terrain segmen…
Ziyong Wang, Yupei Lin, Runxuan Huang, Liang Lin, Dongyu Zhang and others · 2026-10-20
General-purpose multimodal models have shown promise as direct robot action policies, yet whether they grasp the physics underlying real-world interactions and can apply that understanding to guide action remains underexplored. We investigate this question using contact-rich mani…
Charbel Abi Hana, Rayan Zaitouni, Kameel Amareen, Mohamad Mostafa, Hasan Sayour and others · 2026-10-20
Classical robot navigation stacks are reliable but often require re-engineering for each new robot type and site. Learning-based navigation shifts this effort from hand-tuned parameters to data generation and training, where a policy adapts to each new embodiment or facility by f…
Zhuohao Li, Jiawei He, Yuxuan Yan, Lang Zhou, Yinghao Li and others · 2026-10-20
World-Action Models (WAMs) have emerged as a promising paradigm for embodied generalist policies by jointly modeling future world evolution and robot actions across diverse manipulation tasks. However, representing diverse futures is only the first step: a capable WAM must also g…
Youmna Farag, Svetlana Stoyanchev, Simon Keizer, Mohan Li, Rama Doddipatla · 2026-10-20
Reasoning about safety is essential for embodied agents powered by large language models (LLMs). We introduce the Contextual Action and Plan Safety benchmark (CAPSafe), designed to evaluate safety reasoning at two levels: (1) an action-level dataset that tests whether a single ac…
Dhruv Metha Ramesh, Tri-An Nguyen, Junshan Huang, Jingjin Yu, Kostas E. Bekris and others · 2026-10-20
Navigation Among Movable Obstacles (NAMO) requires a robot to rearrange objects that block access to its goal. For a pushing robot, progress may depend on a setup push that enables a later opening without itself clearing a passage. The number of candidate sequences can grow expon…
Yuri De Santis, Michele Pierallini, Franco Angelini, Manolo Garabini · 2026-10-20
Trajectory tracking for compliant robots is challenging due to nonlinear dynamics and modeling uncertainties, often requiring learning-based methods to avoid explicit parameter identification. Iterative Learning Control (ILC) ensures theoretically high precision and preserves the…
Bin-Bin Hu, Shuwei Pei, Saeed Ahmed, Bayu Jayawardhana · 2026-10-20
We propose a distributed platoon controller that guarantees safe and smooth crossing of multi-vehicle systems at an unsignalized intersection. To support the stability analysis, we consider a multi-vehicle platoon maneuvering along a closed path with a single unsignalized interse…
Youwei Zhou, Bohan Hou, Yankai Fu, Yaoxu Lyu, Yifan Ye and others · 2026-10-20
Recent advances in visuospatial understanding and agentic reasoning enable direct robotic manipulation with general-purpose multimodal models, but their embodied capabilities remain poorly understood. This paper systematically evaluate GPT-6 Astra for closed-loop robotic manipula…
Thomas Kaminsky, Áron Vékássy, Hammad Izhar, Angelia Nedić, Stephanie Gil · 2026-10-20
Robot swarms coordinate through directed, time-varying local communication links, some of which may be compromised to allow the transmission of arbitrary malicious data. We provide an algorithm that acts as a wrapper around robot communication---when a robot seeks to update its s…
Matteo Cercola, Katie Z Luo, Simone Formentin, Marco Pavone · 2026-10-20
Reasoning-augmented vision-language-action (VLA) driving models can perceive and reason about safety-critical hazards yet fail to translate that evidence into safe actions. We characterize this failure as a perception-action binding gap: hazard information is available inside the…
Matteo Cercola, Katie Z Luo, Milan Ganai, John Alora, Simone Formentin and others · 2026-10-20
Vision-Language-Action (VLA) policies increasingly reason before acting, but existing reasoning models compress this intermediate decision into a single representation. When several plans are valid, this commitment hides alternatives that could otherwise be selected or revised. W…
Xiaoxu Xu, Hao Li, Hongtao Zhang, Yili Tang, Jiafei Cao and others · 2026-10-20
Effective manipulation requires a policy to capture not only about the current scene geometry, but also about how that geometry will evolve under interaction. Existing approaches either augment policies with geometric information from current observations or introduce future pred…
Toshiki Tanaka, Takuto Oikawa, Keeni Shruti, Teruhito Iida, Kazuya Yoshida and others · 2026-10-20
The research focuses on the thermal interface design of the HAKUTO Rover Flight Model (FM) and its evaluation using the final thermal vacuum experiment. Initially, an analytical thermal model was developed, dividing the rover into six distinct radiating surfaces. The surface temp…
Shaoyi Zheng, Zhongtao Jiang, Yue Peng, Wenbo Lu, Shunchi Zhang and others · 2026-10-20
Cascade world-action models (WAMs) use predicted visual futures to condition robot actions, but inherit costly iterative inference from diffusion-pretrained experts. We ask what a single-step predicted future must preserve to support accurate single-step action prediction. In the…
Christopher Conrad, Deepak Panda, Weisi Guo, Antonios Tsourdos · 2026-10-20
Safe autonomous navigation of UASs requires tactical deconfliction to address conflicts that strategic coordination cannot prevent. This task is challenging under partial observability, when sensor or traffic data are missing, delayed, or degraded. Geometry-based methods can offe…
Jiatong Li, Jisheng Dang, Qiangqiang Ma, Bimei Wang, Jingwen Zhao and others · 2026-10-20
Vision-language-action (VLA) models have achieved strong performance on standard robotic manipulation benchmarks. However, their reliability can degrade substantially when task conditions deviate from the nominal setting. Perturbations in object positions and task instructions ma…
Elias Fontanari, Pietro Noah Crestaz, Matteo Saveriano, Andrea Del Prete · 2026-10-20
Reach-avoid sets, defining the states from which a task can be completed while avoiding unsafe regions, are fundamental tools for deploying robots in environments subject to disturbances and uncertainty, as well as for runtime task-feasibility monitoring, allowing prediction of w…
Yixiang Zhu, Ruilong Wu, Yonghao Chen, Jiaxiang Zou, Yusong Hu and others · 2026-10-20
Vision-language-action (VLA) policies are commonly deployed with asynchronous inference in real-world settings, where the next action chunk is generated while the robot is still executing the previous one. By the time the new chunk is committed, the robot and scene have changed, …
Tong Zhang, Zhiyuan Shi, Yun Peng, Tao Xie · 2026-10-20
A planner using a learned world model can improve its predicted outcome while choosing a worse action. We study when expected free energy (EFE) can help decide how much computation to spend on a plan. An EFE-inspired objective combines goal distance with disagreement among stocha…
Antonio Langella, Giovanni Minelli, Giovanni B. Dessy, Lorenzo Amatucci, Giulio Turrisi and others · 2026-10-20
Learning loco-manipulation policies requires demonstrations that capture coordinated base and arm motion on the target embodiment. Robot-free interfaces allow demonstrations to be collected independently of the target robot, increasing flexibility but without directly capturing p…
Yeonguk Yu, Jaehong Kim, Byungok Han · 2026-10-20
Tactile representation learning for dexterous manipulation often requires substantial robot-side data collection, limiting its scalability across different hands and sensors. We explore whether large-scale human hand-object interaction (HOI) data can provide a useful prior for ro…
Jingjiao Zhao, Dong Jing, Mingyu Cao, Ruochuan Shi, Xiansheng Chen and others · 2026-10-20
History-dependent robotic manipulation requires task context and interaction history, as the current observation alone is insufficient to determine the next action. Hierarchical frameworks use task memory for high-level subgoal reasoning and low-level policies for execution. Howe…
Amane Yamaguchi, Shohei Kamiguchi, Masashi Hamaya, Shigeo Yoshida, Takuji Narumi and others · 2026-10-20
Imitation learning increasingly relies on novice operators, whose demonstrations fall short of experts' in number and inter-trial consistency. Operators act on what they are shown, yet how the rendering of the robot affects their demonstrations, and the policies trained on them, …
Huanyu Tian, Adriana Namour, Lingyun Zeng, Zicong Wu, Sebastien Ourselin and others · 2026-10-20
General-purpose manipulators can position ophthalmic instruments under a software Remote Centre of Motion (RCM). However, at some reachable configurations that are nonsingular for the tip task alone, the RCM and tip-tracking objectives conflict and the servo oscillates. We formal…
Mihaela-Larisa Clement, Tosca Pierro, Ezio Bartocci, Luca Marzari · 2026-10-20
Hamilton-Jacobi (HJ) reachability analysis offers formal safety guarantees for control systems, but training accurate neural certificates remains difficult, particularly when unsafe regions are hard to characterize. We propose a verification-guided training framework that address…
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