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.

Playful Agentic Robot Learning
Jun 17, 2026Junyi Zhang, Jiaxin Ge, Hanjun Yoo +17Robot Policy Learning Robot Skill Learning
University of California, Berkeley · Impossible Research
VEGA: Learning Navigation VLAs from In-the-Wild Egocentric Video with Geometric Trajectory Supervision
Jun 16, 2026Gershom Seneviratne, Yohan Abeysinghe, Jianyu An +2Robot Policy Learning Trajectory Generation
University of Maryland, College Park
Visual Verification Enables Inference-time Steering and Autonomous Policy Improvement
Jun 16, 2026Mingtong Zhang, Dhruv ShahRobot Policy Learning Robot Policy Adaptation
Princeton University
WireCraft: A Simulation Benchmark for Industrial DLO Manipulation
Jun 16, 2026Chongyu Zhu, Ramy ElMallah, Hyegang Kim +5Robot Policy Learning Robotics Simulation
Department of Mechanical and Industrial Engineering, University of Toronto, Canada · CREFLE Inc., Seongnam, Republic of Korea
When Robots Sleep: Offline Skill Consolidation for Shared-Policy Robot Learning
Jun 16, 2026Nethmi Jayasinghe, Diana Gontero, Amit Ranjan TrivediRobot Policy Learning Continual Robot Learning
Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL, USA.
Where Should Action Generation Begin? A Learnable Source Prior for Generative Robot Policies
Jun 16, 2026Meipo Dai, Qiyuan Zhuang, He-Yang Xu +4Robot Policy Learning Robotic Manipulation
Southeast University · The Chinese University of Hong Kong
ADAPT: Analytical Disturbance-Aware Policy Training for Humanoid Locomotion
Jun 15, 2026Bofan Lyu, Jindou Jia, Kuangji Zuo +7Robotic Control Humanoid Robot Locomotion
1MARS Lab, Nanyang Technological University
RHO: Your Coding Agent is Secretly a Roboticist
Jun 15, 2026Karim Elmaaroufi, Justin Svegliato, Sarunas Kalade +3LLM Agent Harnesses Robotic Manipulation
University of California, Berkeley · Embodied Science · AMD
FlashNav: Training Deployable Robot Navigation Policies in Seconds
Jun 14, 2026Shanze Wang, Yiwei Qian, Xinming Zhang +8Robot Policy Learning Reinforcement Learning
Eastern Institute of Technology, Ningbo · The Hong Kong Polytechnic University · National University of Singapore +2
DragMesh-2: Physically Plausible Dexterous Hand-Object Interaction with Articulated Objects
Jun 13, 2026Tianshan Zhang, Yijia Duan, Yanjun Li +2Robot Policy Learning Dexterous Manipulation
School of Computer Science, Peking University
Inference-time Policy Steering via Vision and Touch
Jun 12, 2026Yilin Wu, Zilin Si, Zeynep Temel +2Robot Policy Learning Robot Policy Adaptation
Carnegie Mellon University
TacStyle: Personalizing Tactile Robot Policies using Structured Behavior Representations
Jun 12, 2026Kevin Robledo, Matías I. Torres Galaz, Kumar Dixhant Rai +3Robot Policy Learning Robot Policy Adaptation
Department of Computer Science California State University, Northridge · Department of Mechanical Engineering California State University, Northridge
Improving Robotic Generalist Policies via Flow Reversal Steering
Jun 11, 2026Andy Tang, William Chen, Andrew Wagenmaker +2RL for Robotics Robot Policy Learning
Stanford University · UC Berkeley
FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning
Jun 10, 2026Steven Oh, Jason Jingzhou Liu, Tony Tao +5Robot Imitation Learning Long-Horizon Robotic Manipulation
Carnegie Mellon University · Waseda University
SAFER-Nav: Enhancing Safety for Visual Robot Navigation via Segmentation-Aware Fine-Tuning
Jun 10, 2026Geonyeong Ko, Giung Lee, Changjoo NamRobot Policy Learning Trajectory Generation
Dept. of Electronic Engineering, Sogang University, Seoul, Korea · Dept. of Computer Science, Rice University, Houston, TX, USA · Vertical Labs, Co., Ltd., Seoul, Korea
Adversarial Vulnerabilities of Learned Telesurgery Policies
Jun 10, 2026Shutong Jin, Ziyang Chen, Preethi Satish +3Robot Policy Learning Adversarial Attacks
University of California, Berkeley · KTH Royal Institute of Technology
Steering Multirobot Behavior via Closed-Loop Affine Activation Editing
Jun 9, 2026Satyajeet Das, Darren Chiu, Shashank Hegde +1Robot Policy Learning Robot Policy Adaptation
Department of Computer Science University of Southern California Los Angeles, CA, USA
Bridging the sim2real gap in the table tennis robot with a transformer-based ball states predictor
Jun 9, 2026Yin Bi, Christian Conti, Bilan Yang +3Robotic Table Tennis Robot Policy Learning
Sony AI, Zürich, Switzerland · Sony AI, Tokyo, Japan
Dynamic Execution Horizon Prediction for Chunk-based Robot Policies
Jun 9, 2026Yuchi Zhao, Miroslav Bogdanovic, Arjun Sohal +5Robot Policy Learning Long-Horizon Robotic Manipulation
Department of Computer Science, University of Toronto, 40 St George St., Toronto, ON M5S 2E4, Canada · Vector Institute for Artificial Intelligence, W1140-108 College St., Schwartz Reisman Innovation Campus, Toronto, ON M5G 0C6, Canada · Acceleration Consortium, 700 University Ave., Toronto, ON M7A 2S4, Canada +7
MODIP: Efficient Model-Based Optimization for Diffusion Policies
Jun 9, 2026Zakariae El Asri, Philippe Gratias-Quiquandon, Nicolas Thome +1Robot Policy Learning Robot Policy Adaptation
Sorbonne Université, CNRS, ISIR, F-75005 Paris, France · Institut Universitaire de France (IUF)
GHOST: Hierarchical Sub-Goal Policies for Generalizing Robot Manipulation
Jun 8, 2026Sriram Krishna, Ben Eisner, Haotian Zhan +5Robot Policy Learning Robot Skill Learning
Robotics Institute, Carnegie Mellon University · UMass Amherst
DexPIE: Stable Dexterous Policy Improvement from Real-World Experience
Jun 8, 2026Ruizhe Liao, Wenrui Chen, Liangji Zeng +4Robot Policy Learning Robotic Manipulation
Hunan University
Targeting World Models to Compromise Robot Learning Pipelines
Jun 8, 2026Ethan Rathbun, Ahmed Agha, Saaduddin Mahmud +3Robot Policy Learning World Models for Robotics
Northeastern University · University of Massachusetts Amherst
$ω$ -EVA: Envision, Verify, and Act with Latent Interactive World Models
Jun 8, 2026Zhenguo Sun, Yu Sun, Hande Huang +1Robot Policy Learning World Models for Robotics
Autonomous Obstacle Removal for Excavators through Policy Learning with Particle Simulation
Jun 8, 2026Yuki Kadokawa, Sandro M. Alcantara Tacora, Taro Abe +4Robot Policy Learning Sim-to-Real Transfer
Nara Institute of Science and Technology · Public Works Research Institute, Ibaraki 300-2621, Japan
Benchmarking Vision-Language-Action Models on SO-101: Failure and Recovery Analysis
Jun 7, 2026Yi Yu, Xinchuan QiuRobot Policy Learning Robotic Manipulation
Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima, Japan
ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies
Jun 7, 2026Bingjia Huang, Xiangyu Li, Xiang Wang +7Robot Policy Learning Robot Failure Detection
Institute for AI Industry Research (AIR), Tsinghua University · University of Electronic Science and Technology of China · Work done during internship at AIR, Tsinghua University. +1
PACT: Self-Evolving Physical Safety Alignment for Diffusion Policies in Embodied Manipulation
Jun 7, 2026Lingxuan Wu, Zijian Zhu, Lizhong Wang +5Robot Policy Learning Diffusion Policy
Dept. of Comp. Sci. and Tech., Institute for AI, Tsinghua-Bosch Joint ML Center, THBI Lab, BNRist Center, Tsinghua University, Beijing, 100084, China · Peng Cheng Laboratory, 518108, China
Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking
Jun 6, 2026Alessandro Montenegro, Shihao Li, Puze Liu +2Robot Trajectory Tracking Humanoid Robot Locomotion
Politecnico di Milano · Tongji University · Technische Universität Darmstadt +2
Disturbance-Aware Aerial Robotics for Ethical Wildlife Monitoring
Jun 6, 2026Mahmut Osmanovic, Isac Paulsson, Teddy LazebnikRL for Robotics Aerial Robotics
Department of Computing, Jonkoping University, Jonkoping, Sweden · Department of Information Systems, University of Haifa, Haifa, Israel

Jun 17, 2026J/K move · Enter open · S save

Playful Agentic Robot Learning

Junyi Zhang, Jiaxin Ge, Hanjun Yoo, Letian Fu+16

University of California, Berkeley · Impossible Research

Robot Policy Learning Robot Skill Learning Agent Skill Learning Robot Learning

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Current agentic robot systems can write executable Code-as-Policy programs, observe feedback, and revise behavior across multiple attempts, but they remain largely task-driven: reusable skills are acquired only after explicit instructions. We study Playful Agentic Robot Learning, where an embodied coding agent uses self-directed play as a continual skill-learning stage before downstream tasks arrive. We introduce RATs, Robotics Agent Teams designed for play-time skill acquisition. During play, RATs proposes novel yet learnable exploratory tasks, plans and executes robot-code policies, verifies intermediate progress, diagnoses failures, retries with dense, step-level feedback, and distills successful executions into a persistent code skill library. At test time, the agent reuses relevant skills from this frozen library to help solve new tasks. Experiments in LIBERO-PRO and MolmoSpaces show that play-learned skills improve held-out downstream tasks over no-play and random-play baselines, with 20.6 and 17.0 percentage-point gains over CaP-Agent0 on LIBERO-PRO and MolmoSpaces, respectively. Moreover, the learned skills can be plugged into other inference-time Code-as-Policy agents by simply retrieving them into the context, improving RoboSuite and real-world transfer by 8.9 and 8.8 points, respectively, without finetuning the underlying model.