Learning from Demonstration

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12 papers in the last four weeks, up 200% on the four weeks before. 0.1% of all new papers.

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Latest papers 93

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EaDex: A Cross-Embodiment Dexterous Manipulation Framework from Low-Cost Demonstrations
Jun 2, 2026Qian Zhao, Xin Tong, Chengdong Wu +2Dexterous Manipulation Cross-Embodiment Robot Learning
Faculty of Robot Science and Engineering, Northeastern University · Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences · School of Automation, Nanjing University of Information Science and Technology
Reinforcement Learning from Cross-domain Videos with Video Prediction Model
Jun 2, 2026Zhao Yang, Xinrui Zu, Jacob E. Kooi +5RL for Robotics Imitation Learning
VU Amsterdam, The Netherlands · ISAE-SUPAERO, France
SeeTraceAct: Visibility-Aware Latent Planning from Cross-Embodiment Demonstration Videos
Jun 1, 2026Jaehyeon Son, Junhyun Kim, Kyle Kam +7Robot Skill Learning Generalization in Robotic Manipulation
Georgia Institute of Technology · Allen Institute for AI · Johns Hopkins University +1
Co-training with Ego-centric Video and Demonstration for Robot Navigation Task
Jun 1, 2026Shoya Kuno, Yumo Ouchi, Kanata SuzukiMobile Robotics Robot Navigation
Spatial Robotics Research Center, Fujitsu Limited., Kanagawa 211-8588, Japan.
Set-Supervised Diffusion Policy: Learning Action-Chunking Diffusion through Corrections
Jun 1, 2026Zhaoting Li, Gang Chen, Javier Alonso-Mora +2Diffusion Policy Robotic Manipulation
Delft University of Technology · University of Stuttgart
Inverse Reinforcement Learning without an Optimal Demonstrator: A Feasible Reward Set Approach
May 29, 2026Kihyun Kim, Shripad Deshmukh, Nikos Vlassis +1Reinforcement Learning Learning from Demonstration
MIT LIDS · University of Massachusetts, Amherst · Adobe Research +1
How Should We Teach Robots? A Comparison of Kinesthetic, Joystick, and Gesture-Based Teaching
May 27, 2026Petr Vanc, Jan Kristof Behrens, Václav Hlaváč +1Robot Teleoperation Robotic Manipulation
Czech Institute of Informatics, Robotics and Cybernetics (CIIRC CTU) Jugoslávských partyzánů 1580/3, 160 00 Dejvice
Adversarial Dual On-Policy Distillation from Expressive Teacher
May 26, 2026Zhenglin Wan, Jingxuan Wu, Xingrui Yu +5Robot Policy Learning Adversarial Imitation Learning
School of Computing, National University of Singapore · Department of Statistics and Operations Research, UNC-Chapel Hill, America · CFAR, Agency for Science, Technology and Research, Singapore +4
Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming
May 26, 2026Oleh Borys, Karla StepanovaRobot Skill Learning Robotic Manipulation
Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague.
Closing the Loop in Teleoperation: Episode-Level Data Quality Assessment and Feedback for High-Quality Demonstration Collection
May 25, 2026Gokul Narayanan, Yash Shahapurkar, Melih Erdogan +2Robot Teleoperation Learning from Demonstration
are with Siemens Corporation.
DemoEvolve: Demonstration-Guided Harness Evolution under Sparse Feedback
May 23, 2026Lirong Che, Yuzhe yang, Peiwen lin +4Agent Harness Optimization LLM Agent Self-Improvement
Tsinghua University · AgiBot
Instrumentation for Imitation Learning: Enhancing Training Datasets for Clothes Hanger Insertion
May 22, 2026Remko Proesmans, Thomas Lips, Francis wyffelsRobot Skill Learning Robotic Insertion
AI and Robotics Lab (IDLab-AIRO), Ghent University—imec, Ghent, Belgium
Robots That Know What to Ask: Recovering Misaligned Rewards through Targeted Explanations
May 21, 2026Helena Merker, Nick Walker, Andreea BobuReinforcement Learning Learning from Demonstration
Massachusetts Institute of Technology
SUGAR: A Scalable Human-Video-Driven Generalizable Humanoid Loco-Manipulation Learning Framework
May 19, 2026Tianshu Wu, Xiangqi Kong, Yue Chen +5Humanoid Loco-Manipulation Long-Horizon Robotic Manipulation
1CFCS, School of Computer Science, Peking University · School of Computer Science and Engineering, Beihang University
From a Single Demonstration to a General Policy for Contact-Rich Manipulation
May 17, 2026Xing Li, Oliver BrockRobot Skill Learning Contact-Rich Robotic Manipulation
Robotics and Biology Laboratory, Technische Universität Berlin · Technische Universität Berlin · Science of Intelligence, Research Cluster of Excellence, Berlin +1
SkiP: When to Skip and When to Refine for Efficient Robot Manipulation
May 15, 2026Mingtong Dai, Guanqi Peng, Yongjie Bai +5Robot Skill Learning Contact-Rich Robotic Manipulation
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences · Southern University of Science and Technology · Peng Cheng Laboratory +2
Ergodic Imitation for Adaptive Exploration around Demonstrations
May 13, 2026Ziyi Xu, Cem Bilaloglu, Yiming Li +1Imitation Learning Learning from Demonstration
Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland · Idiap Research Institute, Martigny, Switzerland
Intrinsic Vicarious Conditioning for Deep Reinforcement Learning
May 12, 2026Rodney A Sanchez, Ferat Sahin, Alex Ororbia +1Reinforcement Learning Intrinsic Motivation
Department of Electrical and Microelectronic Engineering, Rochester Institute of Technology · Department of Computer Science, Rochester Institute of Technology
XQCfD: Accelerating Fast Actor-Critic Algorithms with Prior Data and Prior Policies
May 11, 2026Daniel Palenicek, Florian Vogt, Joe Watson +3Robotic RL Learning from Demonstration
Technical University of Darmstadt · KTH Royal Institute of Technology · University of Oxford +2
Data-Asymmetric Latent Imagination and Reranking for 3D Robotic Imitation Learning
May 11, 2026Lianghao Luo, Xizhou Bu, Ruyan Liu +5Robot Skill Learning World Models
Morphi
Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift
May 9, 2026Surbhi Goel, Jonathan Pei, James WangImitation Learning Learning from Demonstration
How to Utilize Failure Demo Data?: Effective Data Selection for Imitation Learning Using Distribution Differences in Attention Mechanism
May 8, 2026Kana Miyamoto, Kanata Suzuki, Tetsuya OgataLearning from Demonstration Robot Imitation Learning
Faculty of Science and Engineering, Waseda University, Tokyo, Japan · Physical AI Laboratory, Fujitsu Limited, Kanagawa, Japan
cotomi Act: Learning to Automate Work by Watching You
May 4, 2026Masafumi Oyamada, Kunihiro Takeoka, Kosuke Akimoto +5Agent Memory Computer-Use Agents
NEC Corporation
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 Human-Intention Priors from Large-Scale Human Demonstrations for Robotic Manipulation
Apr 27, 2026Yifan Xie, YuAn Wang, Guangyu Chen +3Human Motion Generation Cross-Embodiment Robot Learning
1Tsinghua University · 2ByteDance
Learning from Demonstration with Failure Awareness for Safe Robot Navigation
Apr 25, 2026Xianghui Wang, Siwei Cheng, Shanze Wang +3Safe Robot Navigation Offline RL
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong · College of Information Science and Technology, Eastern Institute of Technology, Ningbo, China · School of Computer Science and Technology, University of Science and Technology of China, Hefei, China +1
SpanVLA: Learning from Negative-Recovery Samples with Fast Action Bridging for Vision-Language-Action Model
Apr 21, 2026Zewei Zhou, Ruining Yang, Xuewei Qi +7VLMs for Autonomous Driving Autonomous Driving
Tony · University of California, Los Angeles, USA · Motional, USA +1
Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning
Apr 18, 2026Koki Yamane, Cristian C. Beltran-Hernandez, Steven Oh +2Robot Trajectory Tracking Contact-Rich Robotic Manipulation
OMRON SINIC X Corporation, Bunkyo-ku, Tokyo 113-0033, Japan · Waseda University, Shinjuku-ku, Tokyo, 169-8050, Japan · University of Tsukuba, Tsukuba, Ibaraki 305-8573, Japan
EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World
Apr 8, 2026Ryan Punamiya, Simar Kareer, Zeyi Liu +37Robot Skill Learning Robotic Data Collection
Georgia Institute of Technology · Stanford University · University of California San Diego +5
See and Switch: Vision-Based Branching for Interactive Robot-Skill Programming
Mar 9, 2026Petr Vanc, Jan Kristof Behrens, Václav Hlaváč +1Robot Skill Learning Robotic Manipulation
imitrob.ciirc.cvut.cz/publications/seeandswitch

Jun 2, 2026, cs.ROJ/K move · Enter open · S save

EaDex: A Cross-Embodiment Dexterous Manipulation Framework from Low-Cost Demonstrations

Qian Zhao, Xin Tong, Chengdong Wu, Yang Yang+1

Faculty of Robot Science and Engineering, Northeastern University · Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences · School of Automation, Nanjing University of Information Science and Technology

Dexterous Manipulation Cross-Embodiment Robot Learning Learning from Demonstration Articulated Object Manipulation

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Dexterous manipulation learning has long been hindered by the high costs of data and training, as pure reinforcement learning typically requires large-scale interactive exploration and imitation learning depends on high-quality demonstrations that are expensive to collect. To address this problem, we propose EaDex, a multi-embodiment dexterous manipulation learning framework under low-cost demonstration conditions, which enables rapid generation of demonstration data and consequently reduces training time for efficient dexterous manipulation. At the data level, EaDex captures human hand motions using only a single RGB-D camera and constructs structured demonstration data through MANO-based hand modeling, data normalization, and motion retargeting. At the learning level, we introduce a contact-reward-based dynamic demonstration annealing mechanism, which guides early-stage exploration under demonstration and gradually transitions to autonomous optimization with accumulating contact rewards. Using our custom dataset, we evaluate EaDex on three dexterous hands and three articulated object-opening tasks, covering nine cross-embodiment manipulation settings, achieving a 55.3% relative improvement over the baseline without demonstration annealing. These results validate the effectiveness of the proposed low-cost demonstration pipeline and the dynamic demonstration annealing strategy for dexterous manipulation learning.

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Latest papers 93