Robot Learning

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Robot learning studies how robots can turn observations and experience into actions, from precise manipulation and locomotion to navigation and human-robot interaction. Common approaches include imitation learning from human or robot demonstrations, reinforcement learning through interaction, model-based control, and vision-language-action models that connect instructions and images to actions. Researchers also use reusable skills, structured physical representations, tactile sensing, and simulation or world models to improve transfer across tasks, objects, and robot bodies. A central challenge is making policies reliable beyond their training data: larger and more varied datasets can help, but physical feasibility, sim-to-real transfer, long-horizon recovery, and evaluation that predicts real-world success remain open concerns.

Latest papers

321 papers75 papers in the last four weeks, up 79% on the four weeks before. 0.4% of all new papers.

HABIT: Human-Aware Behavior and Interaction Training Dataset for Robot Manipulation
Jun 30, 2026Jaehwi Song, Suchae Jeong, Byeongguk Jeon +4Robot Policy Learning Robotic Data Collection
Config · Config, KAIST
Multisensory Continual Learning: Adapting Pretrained Visuomotor Policies to Force
Jun 29, 2026Jaden Clark, Changhao Wang, Yihuai Gao +5Robot Policy Learning Continual Robot Learning
Stanford University
SIR: Structured Image Representations for Explainable Robot Learning
Jun 29, 2026Paul Mattes, Jan Schwab, Jens Bosch +5Robot Policy Learning Graph Representation Learning
Intuitive Robots Lab, Karlsruhe Institute of Technology, Germany · Robotics Institute Germany
STEAM: Self-Supervised Temporal Ensemble Advantage Modeling for Real-World Robot Learning
Jun 29, 2026Zhihao Liu, Qiuyi Gu, Yitao Wang +16Robot Policy Learning Robot Skill Learning
Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences · Zhongguancun Academy +8
Analytic Concept-Centric Memory for Agentic Embodied Manipulation
Jun 29, 2026Mingyang Sun, Xiujian Liang, Jiude Wei +4Agent Memory Long-Horizon Robotic Manipulation
Zhejiang University, Hangzhou, China · Shanghai Innovation Institute, Shanghai, China · Westlake University, Hangzhou, China +2
Continual Robot Policy Learning via Variational Neural Dynamics
Jun 25, 2026Jiaxu Xing, Zhiyuan Zhu, Yunfan Ren +4Robot Policy Learning Robot Policy Adaptation
Robotics and Perception Group, University of Zurich
Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?
Jun 24, 2026Tyler Ga Wei Lum, Kushal Kedia, C. Karen Liu +1RL for Robotics Robot Skill Learning
Stanford University · Cornell University
USS: Unifying Spatial-Semantic Prompting for End to End Embodied Visual Tracking
Jun 24, 2026Yuchen Xie, Xinyu Zhou, Kuangji Zuo +4Visual Object Tracking Vision-Based Robot Control
Nanyang Technological University
GRAFT: Graph-Based Affordance Transfer via Part Correspondence
Jun 23, 2026Mengying Lin, Utkarsh Mishra, Ajay Mandlekar +1Zero-Shot Robotic Manipulation Generalization in Robotic Manipulation
Georgia Institute of Technology · NVIDIA
Reflective VLA: In-Context Action Consequences Make VLAs Generalize
Jun 23, 2026Qing Lian, Kent Yu, Lei ZhangICL for Robotics Generalization in Robotic Manipulation
Futian Laboratory · International Digital Economy Academy (IDEA) · Visincept
InSight: Self-Guided Skill Acquisition via Steerable VLAs
Jun 23, 2026Maggie Wang, Lars Osterberg, Stephen Tian +5Robot Skill Learning Large Language Model-Based Robot Planning
Stanford University · Princeton University
World Value Models for Robotic Manipulation
Jun 23, 2026Zhihao Wang, Jianxiong Li, Yu Cui +4Robot Policy Learning World Models for Robotics
ByteDance Seed · Tsinghua University · Peking University
Supervise What Survives: Geometry-Guided VLA Adaptation from Synthetic Robot Videos
Jun 23, 2026Danze Chen, Yanzhe Chen, Qiming Huang +3Visuomotor Policy Learning Vision-Language-Action Models
Show Lab, National University of Singapore
DynaWM: Dynamics-Aware Distillation with World Model and Momentum Targets for Smooth Locomotion over Continuous Stairs
Jun 23, 2026Haidong Hou, Zhangguo Yu, Hengbo Qi +1Humanoid Robot Locomotion Terrain-Aware Robot Locomotion
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration
Jun 22, 2026Xinghao Zhu, Zixi Liu, Shalin Jain +17Contact-Rich Robotic Manipulation Robotic RL
NVIDIA
Causal Reward World Models: Zero-shot Reward Design for Automated Skill Generation
Jun 22, 2026Yang Yang, Yuchuang Tong, Zhengtao Zhang +6RL for Robotics Robot Skill Learning
Institute of Automation, Chinese Academy of Sciences, Beijing 100049, China · Intelligent Manufacturing Institute, HFUT, Hefei 230051, China · School of Electrical Engineering and Automation, Anhui University, Hefei 230061, China +1
Joint Discovery of Object and Action Symbols through Effect Prediction for Robotic Manipulation Planning
Jun 22, 2026Burcu Kilic, Berke Kartal, Fatih Dogangun +2Generalization in Robotic Manipulation Robotic Manipulation
Bogazici University · Ozyegin University · Osaka University
Asymmetric physics enables efficient learning in quadrupedal robot swarms
Jun 22, 2026Yuang Zhang, Yunlong Song, Zhihao He +7Multi-Robot Systems Swarm Robotics
Shanghai Jiao Tong University, Shanghai, China. · Independent Researcher.
How Should a Simulation-to-Reality Transfer Budget Be Spent?
Jun 20, 2026Syed Hamzah Rizvi, Yash Vardhan TomarRobot Dynamics Identification Domain Randomization
Purdue University West Lafayette, USA
Robot Self-Improvement via Human-Video Dynamics Models
Jun 19, 2026Hanzhi Chen, Anran Zhang, Simon Schaefer +5Robot Skill Learning Policy Optimization
1ETH Zurich · 2Technical University of Munich · 4MCML +1
HumanScale: Egocentric Human Video Can Outperform Real-Robot Data for Embodied Pretraining
Jun 18, 2026Juncheng Ma, Jianxin Bi, Yufan Deng +19Robot Foundation Models Cross-Embodiment Robot Learning
PKU · NUS · MIT +2
MirrorDuo: Reflection-Consistent Visuomotor Learning from Mirrored Demonstration Pairs
Jun 18, 2026Zheyu Zhuang, Ruiyu Wang, Giovanni Luca Marchetti +2Robot Skill Learning Generalization in Robotic Manipulation
Division of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden · Department of Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden
Playful Agentic Robot Learning
Jun 17, 2026Junyi Zhang, Jiaxin Ge, Hanjun Yoo +17Robot Policy Learning Robot Skill Learning
University of California, Berkeley · Impossible Research
DiffusionVS: A Generative Framework for Robust Visual Servoing Based on Diffusion Policy
Jun 17, 2026Hongkang Cui, Rui He, Haoyao ChenDiffusion Policy Robot Policy Generalization
Damage Adaptation in Seconds for Architected Materials
Jun 16, 2026James Avtges, Jake Ketchum, Helena Young +3Adaptive Learning Soft Robotics
Northwestern University, Evanston, IL, USA
EgoInfinity: A Web-Scale 4D Hand-Object Interaction Data Engine for Any-View Robot Retargeting and Video-to-Action Robot Learning
Jun 16, 2026Gaotian Wang, Kejia Ren, Andrew Morgan +4Robotic Data Collection Hand-Object Interaction Reconstruction
Rice University · Robotics and AI Institute
Unified Motion-Action Modeling for Heterogeneous Robot Learning
Jun 15, 2026Yunhao Cao, Shitong Liu, Chao Feng +4Robotic Control Robot Dynamics Learning
Cornell University
JoyAI-Sim: A Simulation-Enabled Interconversion Toolchain for the Embodied Data Pyramid
Jun 15, 2026Peidong Liu, Yongce Liu, Songyan Guo +34Robot Learning Robotic Data Collection
Joy Future Academy, JD Group
Learning New Tasks via Reusable Skills: Skill-Compositional Experts for Embodied Continual Learning
Jun 14, 2026Shuaike Zhang, Shaokun Wang, Haoyu Tang +2Continual Robot Learning Robot Skill Learning
Harbin Institute of Technology, Shenzhen · Shandong University · Shenzhen Loop Area Institute
EgoGuide: Egocentric Guidance for Efficient Robot-Free Demonstration Collection and Learning
Jun 12, 2026Yue Xu, Mingtao Nie, Tianle Li +4Robot Learning Robotic Data Collection
Shanghai Jiao Tong University · Beijing Institute for General Artificial Intelligence (BIGAI) · Shanghai Innovation Institute

Jun 30, 2026J/K move · Enter open · S save

HABIT: Human-Aware Behavior and Interaction Training Dataset for Robot Manipulation

Jaehwi Song, Suchae Jeong, Byeongguk Jeon, Sungdong Kim+3

Config · Config, KAIST

Robot Policy Learning Robotic Data Collection Human-Robot Collaboration Human-Robot Interaction Robot Learning Robot Manipulation Datasets

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Large-scale demonstration datasets have been central to recent progress in general-purpose robot policies. However, existing datasets are collected in human-absent settings, and policies trained on such data may perform tasks competently in isolation but fail to exhibit human-aware behaviors. To address this gap, we introduce HABIT, a large-scale robot demonstration dataset for human-present environments. We organize tasks into three roles capturing distinct modes of human-robot interaction: Collaborator, where human and robot jointly accomplish a task; Coworker, where they pursue separate tasks in a shared space; and Supervisor, where the human directs the robot. The dataset comprises over 10K episodes and over 160 hours across 60 tasks. Our experiments show that training on human-present data elicits human-aware behaviors that robot-only data fails to produce: spatiotemporal synchronization in Collaborator tasks, yielding in Coworker tasks, and gesture grounding in Supervisor tasks. Moreover, training on HABIT enables rapid adaptation to new human-robot interaction tasks. By introducing human presence as a new axis of dataset diversity, HABIT extends robot policies to environments shared with humans.