Robotic Manipulation

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Robotic manipulation studies how machines use sensing and motion to change objects and environments, from picking and assembly to tool use and handling deformable materials. Approaches combine geometric planning and force or impedance control with imitation learning, reinforcement learning, diffusion policies, and vision-language-action models; world models, memory, tactile feedback, and human-video demonstrations are also used. Methods are tested in simulation and on real robots using suites such as LIBERO, RoboTwin, ManiSkill, and RLBench, across clutter, bimanual coordination, mobile manipulation, and precision contact tasks. A central challenge is turning benchmark performance into safe, reliable long-horizon behavior across new objects, cameras, robot bodies, and real-world dynamics, while limiting data collection and inference costs.

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1,685 papers341 papers in the last four weeks, up 29% on the four weeks before. 2.0% of all new papers.

The Moving Eye: Enhancing VLA Spatial Generalization via Hybrid Dynamic Data Collection
Jul 2, 2026Jincheng Tang, Yilong Zhu, Zhengyuan Xie +2Generalization in Robotic Manipulation Robotic Manipulation
Lion Rock AI Lab, China Merchants Research Institute of Advanced Technology. · The Hong Kong University of Science and Technology. · Nankai University.
Guided Action Flow: Value-Guided Sampling for Frozen Vision-Language-Action Policies
Jul 2, 2026Liuhaichen Yang, Zhuang Jiang, Chenchao Sheng +8Robotic Manipulation Vision-Language-Action Models
Department of Computer Science, University College London. · Department of Mechanical Engineering, University College London.
One Demonstration Is Enough for Real-World Robotic Reinforcement Learning
Jul 2, 2026Yuwan Liu, Hongze Yu, Song Liu +5Reinforcement Learning Robot Skill Learning
National Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institution of Automation, Chinese Academy of Sciences, Beijing, China · Beijing Academy of Artificial Intelligence, Beijing, China · PKU-PsiBot Joint Lab, Beijing, China +2
FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement
Jul 1, 2026Haoran Hao, Shahram Najam Syed, Jeffrey Ichnowski +1Robot Policy Learning Robot Policy Adaptation
Carnegie Mellon University
AutoSpeed: Annotation-Free Stage-Adaptive Motion Speed Learning for Robot Manipulation
Jul 1, 2026Qingda Hu, Ziheng Qiu, Jieru Zhao +2Robot Skill Learning Visuomotor Policy Learning
College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, China · School of Computing, Shanghai Jiao Tong University, Shanghai, China
Robust Operational Space Control with Conformal Disturbance Bounds for Safe Redundant Manipulation
Jul 1, 2026Wenhua Liu, Fan Zhang, Qin LinRobotic Control Control Barrier Functions
Department of Engineering Technology and the Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA.
3D Point World Models: Point Completion Enables More Accurate Dynamics Learning
Jun 30, 2026Skand Peri, Hung Nguyen, Chanho Kim +2World Model Learning World Models for Robotics
Oregon State University
DVG-WM: Disentangled Video Generation Enables Efficient Embodied World Model for Robotic Manipulation
Jun 30, 2026Ziyu Shan, Zhenyu Wu, Xiaofeng Wang +2Video Generation Robotic Manipulation
Nanyang Technological University, Singapore · Beijing University of Posts and Telecommunications, Beijing, China · GigaAI
Freeform Preference Learning for Robotic Manipulation
Jun 30, 2026Marcel Torne, Anubha Mahajan, Abhijnya Bhat +1Long-Horizon Robotic Manipulation Robot Skill Learning
Stanford University
OopsieVerse: A Safety Benchmark with Damage-Aware Simulation for Robot Manipulation
Jun 30, 2026Arnav Balaji, Arpit Bahety, Sriniket Ambatipudi +3Robotics Simulation Robot Safety
The University of Texas at Austin
CoDex: Learning Compositional Dexterous Functional Manipulation without Demonstrations
Jun 30, 2026Bowen Jiang, William Painter Reger, Roberto Martin-MartinDexterous Manipulation Robotic Manipulation
Robot Interactive Intelligence Lab (RobIn), University of Texas at Austin
Z-1: Efficient Reinforcement Learning for Vision-Language-Action Models
Jun 30, 2026Lang Cao, Renhong Chen, Luyi Li +3Robot Skill Learning Efficient VLA Models
Zioneer Robot Team
RoboTacDex: A Dexterous Visual-Tactile-Action Dataset for Humanoid Manipulation
Jun 30, 2026Xinyi Wang, Donghan Li, Zi'Ang Chen +5Robotic Manipulation Bimanual Robotic Manipulation
Fudan University · ByteDance Intelligent Creation · Shanghai AI Laboratory
Robustness of Robotic Manipulation: Foundations and Frontiers
Jun 30, 2026Yifei Dong, Zhanyi Sun, Lujie Yang +5Robotic Control Robotic Manipulation
Duke University, USA · KTH Royal Institute of Technology, Stockholm, Sweden · Massachusetts Institute of Technology, USA +2
ChronoFlow-Policy: Unifying Past-Current-Future Interaction Flow in Visuomotor Policy Learning
Jun 30, 2026Bokai Lin, Yifu Xu, Xinyu Zhan +6Long-Horizon Robotic Manipulation Visuomotor Policy Learning
Shanghai Jiao Tong University, China · Shanghai Innovation Institute, China · Noematrix, China
ELASTIC: Efficiently Learning to Adaptively Scale Test-Time Compute for Generative Control Policies
Jun 30, 2026Andrew Zou Li, Gokul Swamy, Yonatan Bisk +1Diffusion Policy Test-Time RL
Carnegie Mellon University
Efficient Sim-to-Real Transfer of World-Action Models from Synthetic Priors
Jun 30, 2026Zixing Wang, Kausik Sivakumar, Jinghuan Shang +5Sim-to-Real Transfer Action-Conditioned World Models
1Purdue University · 2Robotics and AI Institute
The Quadruped Soft Tail: Compliant Grasping and Swabbing for Contamination Surveys in Harsh Environments
Jun 29, 2026Harald Minde Hansen, Nandita Gallacher, Kristin Y. Pettersen +2Legged Robotics Robotics
Mechatronics, Robotics and Operation Section, European Organization for Nuclear Research (CERN), Meyrin, Switzerland · Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
Sequential Planning via Anchored Robotic Keypoints
Jun 29, 2026Bryce Grant, Aryeh Rothenberg, Logan Senning +3Language-Conditioned Robot Manipulation Generalization in Robotic Manipulation
Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, United States · Department of Mechanical and Aerospace Engineering, Case Western Reserve University, United States
Training Vision-Language-Action Models with Dense Embodied Chain-of-Thought Supervision
Jun 29, 2026Haoyang Li, Guanlin Li, Youhe Feng +9Cross-Embodiment Robot Learning Robotic Manipulation
1Renmin University of China · 2Zhipu AI
What Enables In-Context Behavior Prompting for Manipulation?
Jun 29, 2026Austin Patel, Ben Pekarek, Joel Enrique Castro Hernandez +1Robot Skill Learning Visuomotor Policy Learning
Stanford University · University of California, Berkeley
Vision-Language-Action Models: Experimental Insights from a Real-World UR5 Platform
Jun 29, 2026Mathilde Hochedel, Marc LalondeVLM Adaptation Robotic Manipulation
Luqia Technologies 405 Ogilvy Ave., #101 Montreal, QC, Canada
Chronos: A Physics-Informed Full-History Framework for Non-Markovian Long-Horizon Manipulation
Jun 29, 2026Yulin Zhou, Yimeng Wang, Nengyu Wang +9Robot Policy Learning Long-Horizon Robotic Manipulation
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
SA-VLA: State-aware tokenizer for improving Vision-Language-Action Models' performance
Jun 29, 2026Tengyue Jiang, Chunpu Xu, Jiayue Kang +1Robotic Manipulation Vision-Language-Action Models
2East China University of Science and Technology · 3Hong Kong Polytechnic University · 4Xi’an University of Electronic Science and Technology +1
WARP: Whole-Body Retargeting for Learning from Offline Human Demonstrations
Jun 29, 2026Zhenyang Chen, Chuizheng Kong, Chuye Zhang +4Mobile Manipulation Cross-Embodiment Robot Learning
Georgia Institute of Technology, Atlanta, Georgia 30332
OpenSPM: An Environment-Transferable Robotic Key Spatial Pose Memory and Closed-Loop High-Frequency Flow-Matching Action Generation Model
Jun 29, 2026Iok Tong Lei, Qingchen Xie, Yifan Wang +2Language-Conditioned Robot Manipulation Generalization in Robotic Manipulation
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China · Institute for Artificial Intelligence, Tsinghua University, Beijing 100084, China
Critical Interval MSE: Toward Reliable Offline Validation for Robot Manipulation Policies
Jun 29, 2026Haoxu Huang, Tongsam Zheng, Yifan Chen +2Robot Policy Learning Off-Policy Evaluation
Tsinghua University · Shanghai Qi Zhi Institute · Spirit AI
Trust Your Instincts: Confidence-Driven Test-Time RL for Vision-Language-Action Models
Jun 29, 2026Siyao Chen, Jiakang Yuan, Jiaxin Wang +1Intrinsic Reward Methods Reinforcement Learning
College of Future Information Technology, Fudan University, Shanghai, China · Shanghai Innovation Institute, Shanghai, China · MoShen Intelligence, Shanghai, China
Legible Shared Autonomy: Implicit Communication of Robot Belief through Motion
Jun 29, 2026Jinwei Liu, Pengfei Li, Shaofeng Chen +2Robotic Manipulation Shared Autonomy
Department of Automation, University of Science and Technology of China, Hefei 230027, China · Department of Automation, Hefei University of Technology, Hefei 230009, China · National Key Laboratory of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China
Hierarchical Policy Learning via Spectral Decomposition
Jun 28, 2026Shuxin Cao, Liquan Wang, Walker Byrnes +3Robot Policy Learning Robot Skill Learning
Georgia Institute of Technology, Atlanta, Georgia, USA · Georgia Tech Research Institute, Atlanta, Georgia, USA · Harvard University, Cambridge, Massachusetts, USA

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The Moving Eye: Enhancing VLA Spatial Generalization via Hybrid Dynamic Data Collection

Jincheng Tang, Yilong Zhu, Zhengyuan Xie, Jiang-Jiang Liu+1

Lion Rock AI Lab, China Merchants Research Institute of Advanced Technology. · The Hong Kong University of Science and Technology. · Nankai University.

Generalization in Robotic Manipulation Robotic Manipulation Vision-Language-Action Models Robot Policy Generalization Shortcut Learning Vision-Based Robot Control

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Vision-Language-Action (VLA) models have shown remarkable promise in generalized robotic manipulation. However, their spatial generalization remains fragile. We argue that simply increasing the number of viewpoints is insufficient. Models often fall into the trap of Shortcut Learning, latching onto spurious correlations (e.g., fixed relative poses between objects or between the camera and robot base) rather than learning true spatial relationships. In this work, we propose a data-centric solution to enhance VLA spatial generalization. We utilize a dual-arm setup where one arm performs manipulation while the other serves as a mobile environmental camera. We systematically evaluate three data distribution patterns: Fixed, Multi-Fixed, and Moving Views. Our findings reveal that a hybrid strategy, combining continuous camera motion with diverse static viewpoints, yields the best performance by substantially reducing spurious correlations while maintaining training stability. Our experiments demonstrate that this strategy mitigates spurious correlations, enabling VLAs to generalize to unseen camera poses and object configurations where simply adding more static viewpoints fails. Crucially, we reveal that the susceptibility to shortcut learning and the struggle with spatial generalization are universal characteristics shared across diverse architectures. Consequently, all evaluated models (ACT, Diffusion, and VLA models including Pi0 and Gr00t) benefit significantly from our mixed data strategy.