Robot Policy Learning

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Robot policy learning studies how robots can turn camera, depth, tactile, and proprioceptive inputs into actions for tasks such as manipulation, locomotion, and navigation. Common approaches include behavior cloning from demonstrations, reinforcement learning in simulation or on hardware, and diffusion, flow-matching, or vision-language-action policies that generate action sequences; world models and hierarchical planners can add foresight and long-horizon structure. Researchers evaluate these methods in simulation suites such as LIBERO, CALVIN, RoboTwin, and navigation benchmarks, as well as on physical robots, with growing attention to memory, contact, safety, and recovery from failure. A central tension is how to make policies generalize across tasks, robots, and changing conditions while remaining data-efficient, reactive, reliable, and fast enough to deploy.

Latest papers

377 papers85 papers in the last four weeks, up 47% on the four weeks before. 0.5% of all new papers.

PAPO-VLA: Planning-Aware Policy Optimization for Vision-Language-Action Models
May 19, 2026Peizheng Guo, Jingyao Wang, Changwen Zheng +1Robot Policy Learning Credit Assignment in RL
Institute of Software Chinese Academy of Sciences · University of Chinese Academy of Sciences
Beyond Predefined Learning Objects: A Thinking-Learning Interaction Model for Up-to-Date Autonomous Robot Learning
May 17, 2026Hong SuRobot Policy Learning Continual Robot Learning
School of Computer Science, Chengdu University of Information Technology, Chengdu, China
How to Instruct Your Robot: Dense Language Annotations Power Robot Policy Learning
May 16, 2026Bosung Kim, Ruiyi Wang, David Acuna +5Robot Policy Learning Robot Skill Learning
University of California, San Diego · 2NVIDIA
SADP: Subgoal-Aware Diffusion Policy for Long-Horizon Manipulation Learned from Foundation Model Generated Demonstrations
May 16, 2026Site Hu, Takato HoriiRobot Policy Learning Long-Horizon Robotic Manipulation
Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, Osaka, Japan
Learning Sim-Grounded Policies for Bimanual Rope Manipulation from Human Teleoperation Data
May 15, 2026Gina Wigginghaus, Tim Missal, Berk Guler +2Robot Policy Learning Bimanual Robotic Manipulation
Technical University of Darmstadt · Honda Research Institute Europe GmbH · German Research Center for Artificial Intelligence (DFKI) +2
NavRL++: A System-Level Framework for Improving Sim-to-Real Transfer in Reinforcement Learning-Based Robot Navigation
May 15, 2026Zhefan Xu, Hanyu Jin, Kenji ShimadaRobot Policy Learning Robot Navigation
Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA
IntentVLA: Short-Horizon Intent Modeling for Aliased Robot Manipulation
May 14, 2026Shijie Lian, Bin Yu, Xiaopeng Lin +8Robot Policy Learning Language-Conditioned Robot Manipulation
1HUST · 2ZGCA · 4HIT +7
Guide, Think, Act: Interactive Embodied Reasoning in Vision-Language-Action Models
May 13, 2026Yiran Ling, Qing Lian, Jinghang Li +6Robotic Control Robot Policy Learning
Faculty of Computing, Harbin Institute of Technology · International Digital Economy Academy (IDEA) · National Key Laboratory of Smart Farm Technologies and Systems +4
AttenA+: Rectifying Action Inequality in Robotic Foundation Models
May 13, 2026Daojie Peng, Fulong Ma, Jiahang Cao +7Robot Policy Learning Long-Horizon Robotic Manipulation
1HKUST(GZ) · 2HKU · 3USTC +3
When Absolute State Fails: Evaluating Proprioceptive Encodings for Robust Manipulation
May 13, 2026Maxime Alvarez, Ryo Watanabe, Paul Crook +4Robot Policy Learning Generalization in Robotic Manipulation
TELEXISTENCE Inc, Foundation Model Division, Japan. · The University of Tokyo, Japan.
Guided Streaming Stochastic Interpolant Policy
May 11, 2026Puming Jiang, Meiyi Wang, Kelvin Lin +2Robot Policy Learning Robot Policy Adaptation
School of Computing, National University of Singapore
StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception
May 11, 2026Evans Han, Yunfan Jiang, Yingke Wang +6Robot Policy Learning Visuomotor Policy Learning
1Stanford University · 2Northwestern University · 3Lambda, Inc
Kintsugi: Learning Policies by Repairing Executable Knowledge Bases
May 10, 2026Teng Cao, Yu Deng, Hikaru Shindo +6Robot Policy Learning LLM Agent Self-Improvement
Artificial Intelligence and Machine Learning Lab, Technical University of Darmstadt, Germany · Hessian Center for Artificial Intelligence (hessian.AI), Germany · Department of Computer Science, Technical University of Darmstadt, Germany +6
BEACON: Cross-Domain Co-Training of Generative Robot Policies via Best-Effort Adaptation
May 9, 2026Antong Zhang, Han Qi, Heng YangSynthetic-to-Real Domain Adaptation Robot Policy Learning
Department of Computer Science, Brown University · School of Engineering and Applied Sciences, Harvard University
Trajectory-Consistent Flow Matching for Robust Visuomotor Policy Learning
May 8, 2026Riad Ahmed, Sujosh Nag, Moniruzzaman Akash +2Flow Matching Robot Policy Learning
University of New Hampshire, Durham, NH, USA
Drifting Field Policy: A One-Step Generative Policy via Wasserstein Gradient Flow
May 8, 2026Juil Koo, Mingue Park, Jiwon Choi +2Robot Policy Learning Wasserstein Gradient Flows
KAIST
Good in Bad (GiB): Sifting Through End-user Demonstrations for Learning a Better Policy
May 2, 2026Noushad Sojib, Ola Ghattas, Momotaz BegumRobot Policy Learning Robot Skill Learning
Department of Computer Science, University of New Hampshire, USA
Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies
May 1, 2026Yi Wang, Xinchen Li, Pengwei Xie +13Robot Policy Learning Robotic Manipulation
Shanghai Innovation Institute. · AGIBOT Finch. · Columbia University.
Can Tabular Foundation Models Guide Exploration in Robot Policy Learning?
Apr 30, 2026Buqing Ou, Frederike DümbgenTabular Foundation Models Robot Policy Learning
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA · Inria, Département d’informatique de l’ENS, CNRS, PSL Research University, Paris, France
Reference-Augmented Learning for Precise Tracking Policy of Tendon-Driven Continuum Robots
Apr 28, 2026Ziqing Zou, Ke Qiu, Haojian Lu +2Robot Trajectory Tracking Robot Policy Learning
Department of Control Science and Engineering, Zhejiang University, Hangzhou, China
Tube Diffusion Policy: Reactive Visual-Tactile Policy Learning for Contact-rich Manipulation
Apr 26, 2026Teng Xue, Alberto Rigo, Bingjian Huang +4Robot Policy Learning Contact-Rich Robotic Manipulation
Meta Reality Labs Research · Idiap Research Institute · École Polytechnique Fédérale de Lausanne (EPFL) +2
Hi-WM: Human-in-the-World-Model for Scalable Robot Post-Training
Apr 23, 2026Yaxuan Li, Zhongyi Zhou, Yefei Chen +5Robot Policy Learning Robot Policy Adaptation
Current Robotics · Tsinghua University · Peking University +1
RPG: Robust Policy Gating for Smooth Multi-Skill Transitions in Humanoid Fighting
Apr 23, 2026Yucheng Xin, Jiacheng Bao, Yubo Dong +5Robot Policy Learning Humanoid Robot Control
Center for Artificial Intelligence and Robotics, Shenzhen International Graduate School, Tsinghua University, China · Shanghai AI Laboratory, Shanghai Jiao Tong University
Mask World Model: Predicting What Matters for Robust Robot Policy Learning
Apr 21, 2026Yunfan Lou, Xiaowei Chi, Xiaojie Zhang +9Robot Policy Learning Video Diffusion Models
National University of Singapore, Singapore · Beijing Academy of Artificial Intelligence, Beijing, China · The Hong Kong University of Science and Technology, Hong Kong, China +3
Quadruped Parkour Learning: Sparsely Gated Mixture of Experts with Visual Input
Apr 21, 2026Michael Ziegltrum, Jianhao Jiao, Tianhu Peng +2Robot Policy Learning Sparse Mixture-of-Experts
Robot Perception and Learning Lab, Intelligent Robotics, Department of Computer Science, University College London, Gower Street, WC1E 6BT, London, UK.
StableIDM: Stabilizing Inverse Dynamics Model against Manipulator Truncation via Spatio-Temporal Refinement
Apr 20, 2026Kerui Li, Zhe Jing, Xiaofeng Wang +6Robot Policy Learning Embodied AI
1GigaAI · 3Beijing Institute of Technology · Institute of Automation, Chinese Academy of Sciences
AnchorRefine: Synergy-Manipulation Based on Trajectory Anchor and Residual Refinement for Vision-Language-Action Models
Apr 20, 2026Tingzheng Jia, Kan Guo, Lanping Qian +6Robot Policy Learning Language-Conditioned Robot Manipulation
Beijing University of Technology Beijing, China · Beihang University Beijing, China · Nanjing University of Science and Technology Nanjing, China
Think before Go: Hierarchical Reasoning for Image-goal Navigation
Apr 19, 2026Pengna Li, Kangyi Wu, Shaoqing Xu +5Visual Navigation Robot Policy Learning
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University · Xiaomi EV · University of Macau +1
$π_{0.7}$ : a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
Apr 16, 2026Physical Intelligence, Bo Ai, Ali Amin +85Robot Policy Learning Robot Skill Learning
A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies
Apr 15, 2026Yu Lei, Minghuan Liu, Abhiram Maddukuri +2Robot Policy Learning Synthetic-to-Real Domain Adaptation
Department of Computer Science, The University of Texas at Austin · Amazon FAR · NVIDIA

May 19, 2026J/K move · Enter open · S save

PAPO-VLA: Planning-Aware Policy Optimization for Vision-Language-Action Models

Peizheng Guo, Jingyao Wang, Changwen Zheng, Wenwen Qiang

Institute of Software Chinese Academy of Sciences · University of Chinese Academy of Sciences

Robot Policy Learning Credit Assignment in RL Vision-Language-Action Models RL for VLMs Closed-Loop Control of VLA Models

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Vision-Language-Action (VLA) models show promising ability in language-guided robotic tasks. However, making VLA policies reliable remains challenging, because a manipulation task is completed through closed-loop interaction, where each action affects subsequent execution. To analyze this problem, we revisit VLA policy during execution and argue that a VLA policy acts both as a planner, which makes task-oriented decisions that change the direction of execution, and as an executor, which realizes these decisions through dense continuous actions. This view suggests that improving VLA reliability requires particular attention to planning actions. Existing optimization methods can imitate actions or improve complete trajectories, but they usually do not explicitly identify planning actions or measure their importance for task success. To address this issue, we propose Planning-Aware Policy Optimization for VLA models (PAPO-VLA). PAPO-VLA first identifies planning actions by jointly considering action variation and trajectory outcome, then estimates their importance through causal sufficiency and causal necessity, and finally incorporates this importance into GRPO advantage estimation. In this way, more important planning actions receive stronger optimization emphasis, while the whole trajectory is still optimized by trajectory-level feedback. Experiments on multiple benchmarks demonstrate the effectiveness of PAPO-VLA.