Learning from Demonstration

Follow this topic Ask about this topic

Momentum

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

Jul 13Week of Sep 28

Latest papers 93

All topics

Dex-One2Many: Learning Dexterous Manipulation from a Single Human Demonstration
Oct 8, 2026Jusuk Lee, Sungha Kim, Yeonsoo Park +8Robotic Manipulation Learning from Demonstration
Seoul National University · University of Maryland, College Park · All Purpose AI +1
Learning to Act with Task Progress: Distilling Small Agents from Compact Teacher Supervision
Oct 7, 2026Wenxi GanLearning from Demonstration Agent Skill Learning
City University of Hong Kong Shenzhen Loop Area Institute
Learning Unknown Constraints without Unsafe Data via Optimality and Counterfactual Regularization
Oct 7, 2026Zhouyu Zhang, Chih-Yuan Chiu, Glen ChouConstrained RL Learning from Demonstration
School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta, GA 30309 · School of Cybersecurity and Privacy School of Aerospace Engineering Georgia Institute of Technology
EgoAlign: Bridging the Human-Humanoid Gap for Long-Range Loco-Manipulation
Sep 29, 2026Yiming Jiang, Jin Chen, Chongyang Xu +3Humanoid Loco-Manipulation Long-Horizon Robotic Manipulation
Beihang University · Alibaba Group · Shanghai Innovation Institute +1
Encore: Few-Shot Agentic Discovery of Manipulation Strategies
Sep 29, 2026Yifan Kang, Zihan Wang, Zhiwen Fan +1Robot Policy Learning Language-Conditioned Robot Manipulation
Massachusetts Institute of Technology · AI Open Source Foundation · Texas A&M University +1
Track-and-Complete: Learning Humanoid Skills from a Single Failed Human Video
Sep 29, 2026Sarmad Idrees, Jongeun ChoiRL for Robotics Robot Skill Learning
School of Mechanical Engineering, Yonsei University, Seoul 03722, Korea
AGRO-SUVIDE: Agentic Robotics for Surgical Viscoelastic Debridement
Sep 28, 2026Shutong Jin, Ziyang Chen, Preethi Satish +4Robot Skill Learning Robotics
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA. · Department of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, SE 10044, Sweden. · Intuitive Surgical, Sunnyvale, CA 94086, USA.
The Statistical Benefits of Multiple Responses for Learning from Demonstrations
Sep 27, 2026Chandramauli Chakraborty, Cong MaLearning from Demonstration Statistical Learning Theory
Department of Statistics, University of Chicago
PAKT: Physically-Aligned Kinesthetic Teaching for Reinforcement Learning
Sep 22, 2026Lars Johannsmeier, Yashraj NarangRobotic Control RL for Robotics
NVIDIA
From Instrument-Mounted Demonstrations to In-Vivo Execution: Learning Bimanual Laparoscopic Appendectomy Without Robot-Collected Demonstrations
Sep 22, 2026Dongho Yee, Juahn Oh, Jinseok Lee +10Bimanual Robotic Manipulation Learning from Demonstration
Department of Transdisciplinary Medicine, Seoul National University Hospital · Rosota Inc., Seoul, Republic of Korea · Department of Mechanical Engineering, Seoul National University +8
ShowTellArena: Evaluating Business Workflow Understanding from Demonstrations
Sep 21, 2026David Garg, Ritobrata Sarkar, Ehsan Azarnasab +1AI Agent Evaluation AI Agent Benchmarks
Brackett Labs
Learning Beyond What Humans Can Demonstrate
Sep 21, 2026Yuchen Song, Aditya Mittal, Unnat JainRobotic Data Collection Robotic Manipulation
University of California, Irvine
How to Better Train VLAs: Lessons Learned From the REAL-I Challenge at ICRA 2026
Sep 12, 2026Jiaming Wang, Jizhuo Chen, Diwen Liu +11Robot Policy Adaptation Robotic Policy Evaluation
Monkey See, Can Monkey Do? A Benchmark for Evaluating Robot Skill Learning by Observation
Sep 8, 2026Weiwei Gu, Anmol Gupta, Anant Sah +5Long-Horizon Robotic Manipulation Robot Skill Learning
School of Augmented Intelligence Arizona State University · NVIDIA
State-of-the-Art in Learning-by-Demonstration with Passive Observation for Industrial Assembly Automation
Sep 7, 2026David Koetter, Oliver Petrovic, Christian BrecherGeneralization in Robotic Manipulation Imitation Learning
RWTH Aachen University, Laboratory for Machine Tools and Production Engineering, Steinbachstraße 19, 52074 Aachen, Germany · Fraunhofer Intitute for Production Technology IPT, Steinbachstraße 17, 52074 Aachen, Germany
From Prior-Guided Heuristics to Deployable Agents: Accelerating Demonstration-Driven Reinforcement Learning for Deadline-Constrained Network Control
Sep 3, 2026Vincenzo Norman Vitale, Mohammad Solki, Antonia Maria Tulino +2Learning from Demonstration Multi-Agent Reinforcement Learning
DIETI, University of Naples Federico II, Via Claudio 21, Naples, 80125, Italy · New York University, 6 MetroTech Center, Brooklyn, New York, 11201, USA · University of Southern California, 3740 McClintock Ave., Los Angeles, 90089, California, USA +2
One Demonstration, Many Objects: Generalizing Manipulation via Local Contact Geometry
Sep 1, 2026Satvik Sharma, Samrat Sahoo, Huang Huang +4Contact-Rich Robotic Manipulation Sim-to-Real Transfer
Stanford University
Teach and Grow: An Agent-Centered Architecture for General Robot Learning
Aug 17, 2026Chang Nie, Zhe Liu, Hesheng WangRobot Skill Learning Generalization in Robotic Manipulation
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, and the Shanghai Key Laboratory of Navigation and Location Based Services, Shanghai 200240, China
Enabling Scalable Kinesthetic Teaching via Observer-based Hand-guiding with Active Support
Aug 11, 2026Anna Tuma, Giuseppe Monetti, Jochen J. Steil +1Force-Controlled Robotic Manipulation Learning from Demonstration
Technology & Innovation Center, KUKA, Augsburg, Germany · Institute of Robotics and Process Control, Technische Universität Braunschweig, Braunschweig, Germany
Multiscale Reward Hedging from Correct Demonstrations
Aug 7, 2026Pahan DewasurendraLearning from Demonstration Statistical Learning Theory
Johns Hopkins University
Robot Learning from Human Demonstrations: Handwritten Alphabet Trajectories and Human-Likeness Evaluation
Aug 6, 2026Alperen Kenan, Paul Bremner, Manuel GiulianiRobot Skill Learning Gaussian Mixture Models
Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom · Faculty of Electrical Engineering, Kempten University of Applied Sciences, Kempten, Germany
GORDON: Graph-based Object-centric Rewards for Decomposition of Long-Horizon Manipulation
Aug 4, 2026Andrea Protopapa, Davide Buoso, Francesca Pistilli +2Long-Horizon Robotic Manipulation Learning from Demonstration
Politecnico di Torino, Turin, Italy. · Interactive Robot Perception & Learning (PEARL) Lab, Computer Science Dept., TU Darmstadt, Germany. · Hessian.AI, Darmstadt, Germany. +1
ShadowDancer: Teaching Video World Models Any Action by Learning Unified Dynamics Representations from a Video and Its Shadow
Jul 30, 2026Jin Cao, Zian Meng, Kaipeng ZhangLatent Action Learning Action-Conditioned World Models
1Alaya Lab · 2Shanghai Innovation Institute
DexDirect: Direct Kinesthetic Arm Guidance for Efficient Dexterous Demonstration Collection
Jul 30, 2026Beom Jun Kim, Shiu-Jen Wang, Jonathan Liu +7Dexterous Manipulation Robot Skill Learning
Department of Mechanical and Aerospace Engineering · Department of Electrical and Computer Engineering · Department of Computer Science
Cooperative Multi-UAV Navigation in Complex Environments via Systematic Multi-Agent Deep Reinforcement Learning
Jul 28, 2026Yu Su, Nabil AoufLearning from Demonstration Curriculum Learning
School of Science and Technology, City St George’s University of London, London, United Kingdom
When Does Legacy Data Start to Help? Emergent Transfer in Cross-Configuration Robot Learning
Jul 28, 2026Tao Wang, Hudson Hou, Yingdong Hu +7Robot Skill Learning Robotic Data Collection
Huazhong University of Science and Technology · Spirit AI · Peking University +3
Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations
Jul 28, 2026Yizhou Chen, Hang Xu, Dongjie Yu +7Long-Horizon Robotic Manipulation Robot Skill Learning
The University of Hong Kong, Hong Kong · JD.com (JingDong) · Nanyang Technological University (NTU), Singapore +3
AXIS: A Growable Community-Driven Data Engine for Scalable Robot Manipulation
Jul 23, 2026Mengfei Zhao, Dihong Huang, Yikai Tang +12Robot Skill Learning Robotic Data Collection
1Axis Robotics · University of California, Berkeley · 3Georgia Institute of Technology +6
Learning Diverse Humanoid Tasks via Synthetic Video Scenarios without Real World Data
Jul 22, 2026Yun-Hao Tsai, Cong-Thanh Vu, Yen-Chen LiuSynthetic Data Generation for Robotics Learning from Demonstration
Department of Mechanical Engineering, National Cheng Kung University (NCKU), Tainan 70101, Taiwan
EgoRecovery: Acquiring Failure Recovery Ability Through Human Recovery Demonstration
Jul 22, 2026Zuhao Ge, Yuchen Zhou, Weitao Zhou +8Robot Failure Recovery Cross-Embodiment Robot Learning
Institute of Trustworthy Embodied AI (TEAI), Fudan University · 2Shanghai Key Laboratory of Multimodal Embodied AI · 3Simple AI

Oct 8, 2026, cs.ROJ/K move · Enter open · S save

Dex-One2Many: Learning Dexterous Manipulation from a Single Human Demonstration

Jusuk Lee, Sungha Kim, Yeonsoo Park, Jonguk Cheon+7

Seoul National University · University of Maryland, College Park · All Purpose AI+1

Robotic Manipulation Learning from Demonstration Zero-Shot Robotic Manipulation RL for Robotics Reinforcement Learning Sim-to-Real Transfer

Open paper AskPDF arXiv

While learning dexterous manipulation from a single human video offers a promising alternative to costly robot demonstrations, many recent methods predominantly imitate demonstrated motions. Such strict motion matching often limits generalization to initial object poses, goal poses, and grasps not shown in the video. Alternatively, discovering a policy via reinforcement learning (RL) allows for broad generalization, but without prior guidance, it struggles with high-dimensional exploration in complex, multi-stage tasks. To address these coupled generalization and exploration challenges, we present Dex-One2Many, a real-to-sim-to-real framework that learns a generalizable dexterous manipulation policy from a single human video. Our key insight is to abstract the video into sequential scene graphs that guide RL, enabling efficient exploration while preserving broad generalizability. The graphs serve as generative constraints for sampling diverse reset states and provide dense rewards for each stage. Because the graphs constrain relations rather than exact poses, these reset states cover object poses and grasps beyond the video, while initializing each stage from them with dense rewards keeps exploration short and guided. Trained entirely in simulation, Dex-One2Many transfers zero-shot to a real multi-fingered hand. Across five tool-use and manipulation tasks, Dex-One2Many exceeds baselines by 6.5% in seen configurations, while its robust generalization widens this gap to 71% in unseen scenarios.

Momentum

Related topics

Latest papers 93