Latest Embodied AI Research Papers

The newest Embodied AI papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Embodied AI so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• When Embodied AI Infers Too Much
  Chengxu Zhou · UCL Discovery (University C... · Nov 1, 2026

  As embodied AI systems enter homes, hospitals, and public spaces, they introduce a new class of privacy risks—not by recording more, but by inferring more. Drawing on modalities such as thermal imaging, radio-frequency sensing, chemical ana…

• SwapBot: Tool-Centric Robot Manipulation via Interchangeable End-Effectors and Tactile Sensing
  Yunchu Zhang, Ray Song, Junhan Jiao, Liyiming Ke et al. · arXiv · Oct 29, 2026

  Robot manipulation in everyday environments inherently involves substantial tool usage. Seemingly simple household tasks, such as cooking pan-fried tofu, require forceful cutting, delicate brushing, and precise placement using different too…

• Gym-Anything for Robotics: Scaling physical simulations with digital agents
  Pranjal Aggarwal, Sean Welleck · arXiv · Oct 29, 2026

  Realistic, fast robotics simulations could enable large-scale training of embodied AI models that transfer to the real world. However, despite decades of progress, available simulations cover only a narrow range of real-world tasks and phys…

• A Tutorial on Shape Servoing of Deformable Objects-Part I: Fundamentals
  Ignacio Cuiral-Zueco, Alessio Caporali, Miguel Aranda, Gianluca Palli et al. · arXiv · Oct 29, 2026

  In robotics, shape control (or shape servoing) refers to the automatic manipulation of a deformable object to achieve a desired shape. It involves a feedback loop that exploits the relation between robot motion and changes in the measured s…

• BURGER: Benchmarking and Understanding Robotic Generalization via Embodied Drawing
  Xiangyu Xu, Bangshuai Peng, Ziyi Liu, Mengjie Zhao et al. · arXiv · Oct 29, 2026

  How effectively can vision-language-action (VLA) models learn diverse language-conditioned behaviors as training data scales? Investigating this question requires large and varied robot datasets, whose collection on physical robots remains …

• Improving Fine-Grained Manipulation through Demonstration Expansion and Execution Design
  Jingxu Gu, Dongze Lian · arXiv · Oct 29, 2026

  Fine-grained manipulation requires a robot policy to follow instructions, remain robust to visual variations, and translate predicted actions into precise robot control. We present Demonstration Expansion and Execution Design (DEED), a fram…

• HiFOR: Hierarchical Foresight with Action-Guided World Grounding for Vision-Language-Action Model
  Rongxing Ding, Hongyu Qu, Kehong Wu, Xudong Wang et al. · arXiv · Oct 29, 2026

  Vision-Language-Action (VLA) models have demonstrated promising capabilities in robotic manipulation by integrating visual perception, language understanding, and action generation. Recent advances have further incorporated future predictio…

• TouchBridge: Tactile-Conditioned Human-to-Robot Transfer for Dexterous Manipulation
  Haozhe Zhang, Gu Zhang, Zirui Zou, Yuzheng Chen et al. · arXiv · Oct 29, 2026

  Human demonstrations offer an efficient and scalable source of data for dexterous robot manipulation, but differences in sensing and hand morphology complicate the transfer of contact-rich interactions. We present TouchBridge, a hardware--s…

• LLM-guided Planar Loco-Manipulation for Quadruped Robots
  Georges Jetti, Michael Khayyat, Jean-Pierre Sleiman, Stefano Arrigoni et al. · arXiv · Oct 29, 2026

  Robotic navigation typically assumes a collision-free route to the goal exists; in cluttered environments with movable obstacles, however, reaching the goal may require physical interaction. Classical solutions encode these behaviors throug…

• AcrossDEX 1.0: From Human Motion Pretraining to Bimanual Dexterous Manipulation across Hand Embodiments
  Xiaoyu Yang, Nan Wu · arXiv · Oct 29, 2026

  Bimanual dexterous manipulation requires continuous coordination of wrist motion, finger contact, and object motion. Human videos provide rich manipulation experience. Using this experience for robot control requires aligning human poses wi…

• RoboAtom: Large-scale Atomic-Action Supervision for Robot Learning
  Xueyuan Wei, Xuanxu Wang, Xudong Li, Yuping He et al. · arXiv · Oct 29, 2026

  Fine-grained action annotations can support skill reuse and compositional generalization in robot policies. However, robot datasets typically provide task-level instructions without subtask annotations. Although some include subtask labels,…

• Decision-Level Error Attribution and Depth-Aware Safety Evaluation of Monocular RGB Human Tracking for Autonomous Robot Navigation
  Abdulahi Taiwo, Chijioke Cyriacus Ekechi · arXiv · Oct 29, 2026

  Monocular RGB tracking is useful for robots operating near people, but perception errors affect binary stop/proceed decisions in ways that geometric accuracy metrics do not capture. We examine how much decision error arises from the safety …

• Learning Safe Flow Matching Robot Policies from Binary Feasibility Oracles
  Jianming Ma, Rongjun Jin, Yizhi Chen, Yue Gao · arXiv · Oct 29, 2026

  Robot foundation models increasingly rely on flow-based action generation for general-purpose manipulation, yet the generated actions are not guaranteed to satisfy physical constraints such as joint limits and collision avoidance. This prob…

• Don't Risk It!: Trajectory-Conditioned Distributional Risk for LiDAR-Only Crowd Navigation
  Mathai Mathew Pulicken, Laksh Nanwani, Basant Sharma, Phani Teja Singamaneni et al. · arXiv · Oct 29, 2026

  Efficient crowd navigation requires anticipating dynamic collision-free space without relying on fragile object tracking. We propose the Don't Risk It Planner (DRIP), an end-to-end LiDAR-only planning framework that couples occupancy foreca…

• ThinkWorld: World Imagination Reinforces Chain-of-Dynamics Reasoning
  Chaehyun Kim, Chi-Pin Huang, Jisoo Kim, Min-Hung Chen et al. · arXiv · Oct 27, 2026

  Vision-Language-Action (VLA) models have made substantial progress in robotic manipulation, but their generalization remains constrained by the diversity and coverage of action-labeled robot demonstrations. Human videos provide much broader…

• Pushing Dexterity and Safety for In-Hand Object Pose Reaching That Works Remarkably Well
  Ian Chuang, Hiya Shah, Vamsi Chilamkur, Iman Soltani et al. · arXiv · Oct 27, 2026

  Dexterous manipulation with multi-fingered hands is the next frontier of robotics, yet learned hand skills remain limited to simple grasps or basic repeated motions, such as rotating an object. As robots attempt more demanding in-hand maneu…

• EgoThumb: A Vision-Driven Supernumerary Robotic Thumb for Autonomous Grasp and Holding Assistance
  Jianyu Hou, Jiawei Fu, Weikang Wan, Minghui Jiang et al. · arXiv · Oct 27, 2026

  Supernumerary robotic fingers augment human dexterity by enabling a single hand to perform complex "hold-and-manipulate" tasks that typically require bimanual coordination. However, existing devices are often hindered by bulky hardware that…

• Kinematic Constraint-Referenced Evaluation for Articulated Object Manipulation
  Baptiste Bonin, Caro Strickland, Audrey Durand · arXiv · Oct 27, 2026

  Manipulation policies are evaluated predominantly by binary task success, which cannot tell whether the commanded gesture is the one the object's kinematics admit. We introduce Constraint-Referenced Strategy (CRS), which projects the comman…

• When, Where, and What to Correct: Human-Guided Adaptive Residual Correction for Robot Policies
  Anukriti Singh, Maxime Caniot, Pratap Tokekar, Firas Abi-Farraj · arXiv · Oct 27, 2026

  Imitation policies that reliably execute most of a manipulation trajectory can still fail in localized regions requiring last-mile precision. Updating the full policy or exploring residual actions everywhere treats this local failure as a g…

• Rendering-Free Event Frame Simulation for Fast Reinforcement Learning of Autonomous Robot Navigation
  Dequan Ou, Austin Lopez, Quentin Missinne, Christophe De Wagter et al. · arXiv · Oct 27, 2026

  Event cameras offer low latency, high dynamic range, and milliwatt-level power consumption, making them ideal for agile robots such as tiny drones navigating through cluttered environments. However, existing simulators generally render phot…

• Which Hand Was Late? Diagnosis for Robot Failure Recovery in Bimanual Tool Use
  Baptiste Bonin, Caro Strickland, Audrey Durand · arXiv · Oct 27, 2026

  Failure diagnosers for robot manipulation are increasingly vision--language models (VLMs), trained and evaluated on single-arm failures produced by perturbing a successful demonstration. Bimanual tool use adds failures of other kinds: a too…

• Ego-CEVR: Learning Cross-Embodiment Visual Representations from Human Egocentric Videos
  Keru Zhou, Zhirui Fang, Xinrun Xu, Yaopeng Lou et al. · arXiv · Oct 27, 2026

  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 co…

• VLA-Stem: A Lightweight Safety Mechanism for Vision-Language-Action Models via Reinforcement Learning
  Bojun Zou, Wenhao Yu, Jiaohao Li, Jiaxuan Lin et al. · arXiv · Oct 27, 2026

  Vision-Language-Action (VLA) models integrate high-level reasoning with low-level action execution for end-to-end robotic control. However, most VLA models target static manipulation in unconstrained environments, overlooking spatial constr…

• Tokens of Motion: Primitive Motion Learning for Human-Humanoid Skill Transfer
  Hanwen Ren, Meng Zheng, Wenzhe Cui, Benjamin Planche et al. · arXiv · Oct 27, 2026

  Vision-language-action (VLA) models provide a promising paradigm for scalable robotic manipulation, yet their reliance on success-only behavioral cloning leaves them brittle; lacking corrective training signals, minor execution errors rapid…

• KEPT: Conformal Keepouts Kept in Planning for Enclosed-Space Navigation in Degraded Sensing
  Eric Minwoo Kim, Jong-Kook Kim · arXiv · Oct 27, 2026

  Autonomous vehicles in enclosed spaces such as tunnels and mines rely on range maps that smoke, dust and fog make inaccurate. Conformal prediction calibrates a keepout around the mapped walls that covers the true unsafe set at a chosen prob…

• MCP-Net: Real-Time Coronary Perception Network and Topological Analysis for Robotic Guidewire Navigation
  Sahar Nasirihaghighi, Tianyuan Wang, Kenan Niu, Stefano Stramigioli · arXiv · Oct 27, 2026

  Coronary guidewire navigation during percutaneous coronary intervention requires continuous interpretation of vessel anatomy, guidewire position, and target stenosis under two-dimensional X-ray fluoroscopy. While existing approaches commonl…

• Learning Force-Reactive Humanoid Loco-Manipulation
  Sihang Li, Xiaofeng Guo, Jinkun Cao, Charlie Cheng et al. · arXiv · Oct 26, 2026

  Many everyday tasks require controlling contact force as well as motion. On a humanoid, this force must often be coordinated across both hands and the whole body, and cannot be specified by kinematic trajectories alone. Therefore, we presen…

• Profiling a Frontier Agent with EBench: From Direct Control to Harness with Policy
  Yifei Yao, Yude Zou, Guanbo Jiao, Xing Gao et al. · arXiv · Oct 26, 2026

  Frontier multimodal large language models (MLLMs) can control robots without robot-specific training, but how their capabilities compare and interact with post-trained vision-language-action (VLA) and world action model (WAM) policies acros…

• Continuous-Time Safe Planning and Control for Agile Quadrupeds
  Gage MacLin, Mason Peterson, Camilla Tabasso, Venanzio Cichella et al. · arXiv · Oct 26, 2026

  Agile quadruped navigation in dynamic environments demands trajectories that are both dynamically feasible and provably collision-free. Most real-time planners achieve tractability through a convex reduction that requires simplified kinemat…

• VPR-Space: A Benchmark for Visual Place Recognition in Space Analog Domains
  Richard Fechner, Peer Neubert · arXiv · Oct 26, 2026

  Visual Place Recognition (VPR) is an important means for loop closure detection in visual Simultaneous Localization and Mapping (SLAM). While state-of-the-art VPR and SLAM approaches excel in urban and feature-rich natural environments, the…