Robot Imitation Learning

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172 papers in the last four weeks, up 310% on the four weeks before. 1.0% of all new papers.

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AeroManip-VLA: Scalable Vision-Language-Action Learning for Aerial Manipulation with RL-Generated Demonstrations
Sep 29, 2026Rui Huang, Yanlin Mu, Lidong Li +3Synthetic Data Generation for Robotics Vision-Language-Action Models
National University of Singapore · Beijing Institute of Technology
VidAct: Learning Manipulation from In-the-Wild Videos with Object-Centric 3D Awareness
Sep 29, 2026Hang Li, Mingxin Zhang, Zihan Wu +9Object-Centric Representation Learning Robotic Manipulation
Technical University of Munich · Peking University · Huawei Heisenberg Research Center +2
SAKI: Skill Assembly and Kinematic Imitation from Human Videos for Long-Horizon Mobile Manipulation
Sep 28, 2026Yijie Lu, James Zhao, Weiming ZhiLong-Horizon Robotic Manipulation Mobile Manipulation
School of Computer Science, The University of Sydney, Australia · Australian Centre for Robotics, The University of Sydney, Australia
DexRoam: Learning Mobile Bimanual Dexterous Manipulation from Egocentric Whole-Body Human Demonstrations
Sep 28, 2026Rui Zhou, Yibo Yuan, Junkai Zhao +4Mobile Manipulation Bimanual Robotic Manipulation
The Hong Kong University of Science and Technology · Beijing Academy of Artificial Intelligence · Beihang University +2
Agent Priors-guided Policy Learning
Sep 28, 2026Puming Jiang, Tianrun Hu, Haozhe Du +4Robot Skill Learning Robot Imitation Learning
National University of Singapore
From Pixel to Poses: Object-centric Tool Manipulation Learning from Human Demonstrations
Sep 28, 2026Bangjun Wang, Longyan Wu, Yukun Wei +8Object-Centric World Models Robotic Tool Use
The University of Hong Kong · Shanghai Innovation Institute · Xiaomi EV
DexAgent: An Agentic Human2Sim2Robot Framework for Dexterous Manipulation with Self-Evolving Tool Library
Sep 28, 2026Youhui Wang, Yunzhu Li, Li Fei-Fei +2Long-Horizon Robotic Manipulation Robotics Simulation
Stanford University · Columbia University
DexWeave: Learning Dexterous Humanoid Loco-Manipulation from Human Demonstrations
Sep 28, 2026Naichuan Sun, Haotian Shen, Yizhang Zhang +5Humanoid Loco-Manipulation Cross-Embodiment Robot Learning
Department of Artificial Intelligence, School of Engineering, Westlake University · School of Artificial Intelligence, Shanghai Jiao Tong University
SynIL: Leveraging Synergy for Offline Imitation Learning from Imperfect Demonstration Datasets
Sep 28, 2026Yuto Tanaka, Kyo Kutsuzawa, Martina Doku +2Robot Imitation Learning Robotic Control
Department of Robotics, Graduate School of Engineering, Tohoku University, 6-6-01 Aoba, Aramaki, Aoba-ku, Sendai, 980-8579, Miyagi, Japan
UMR: Universal Manipulation Representation
Sep 28, 2026Song Liu, Linyi Li, Yanshun Zhao +11Zero-Shot Robotic Manipulation Generalization in Robotic Manipulation
University of Science and Technology of China, Hefei, China. · Suzhou Artificial Intelligence Laboratory, Suzhou, China.
GAE: General Action Expert for Real-Time Humanoid Teleoperation
Sep 28, 2026Yuefan Wang, Huaicheng Zhou, Xiao He +6Robot Teleoperation Humanoid Robot Control
Westlake Robotics · Westlake University
Unified Visual-Tactile-Action Modeling from Human Demonstrations for Dexterous Manipulation
Sep 28, 2026Wenqiao Li, Qianyou Zhao, Jiawen Hao +5Contact-Rich Robotic Manipulation Visuo-Tactile Representation Learning
Shanghai Jiao Tong University · Sharpa Robotics · Beijing Institute of Technology +1
TacGooseBumps (TacGB): Retrofitting Normal-Only Tactile Sensors with Shear Encoding for Learning Contact-Rich Manipulation
Sep 27, 2026Wenjie Li, Binyu Yang, Yuxin Chen +2Tactile Sensing Contact-Rich Robotic Manipulation
University of California, Berkeley · Saratoga High School
DexTaG: Tactile-as-Guidance in Reinforcement Learning for Dexterous Manipulation
Sep 27, 2026Han Yang, Yian Wang, Yunlong Song +2Reinforcement Learning Contact-Rich Robotic Manipulation
University of Massachusetts Amherst. · Genesis AI.
FoLD: Force-Informed Learning for Dexterous Articulated Object Manipulation
Sep 27, 2026Haowei Shen, Tingai Li, Yumeng Liu +6Cross-Embodiment Robot Learning Robotic Manipulation
University of Science and Technology of China · Institute of AI for Industries, Chinese Academy of Sciences · Jiangsu Key Laboratory of AI for Industries +1
Beyond Conservatism: Recoverability-Conditioned Exploration for Model-Based Imitation Learning
Sep 27, 2026Xuanlin Chen, Ziyue Wang, Xunlan Zhou +3Imitation Learning RL Exploration
School of Intelligent Science and Technology, Nanjing University, China · National Key Laboratory for Novel Software Technology, School of Artificial Intelligence, Nanjing University, China
TimelyDAgger: Timing-Aware Expert Querying for VLA Policy Improvement
Sep 27, 2026Zhixuan Zhao, Peiyan Li, Enhao Zhang +10Robot Policy Learning Robot Imitation Learning
Tsinghua University · SEEN·E Robotics · Imperial College London +3
Beyond State-as-Action: Exploiting Command-State Discrepancy for Robot Imitation Learning
Sep 27, 2026Peiyan Li, Yueran Tao, Enhao Zhang +10Robot Policy Learning Robot Imitation Learning
Tsinghua University · SEEN·E Robotics · Imperial College London +3
Is Online Interaction Necessary for Recovery? A Minimalist Approach to Robust Planning via Perturbation
Sep 27, 2026Bumgeun Park, Donghwan LeeRobot Imitation Learning Behavior Cloning
Korea Advanced Institute of Science and Technology (KAIST)
CAPEX: Efficiently Distilling Foundation Model Behavior into Deployable Robot Policies through Experience-Adaptive Reasoning
Sep 26, 2026Shivam Aarya, Zhang Xi-Jia, Chengyue Huang +6Synthetic Data Generation for Robotics Data-Efficient Robot Learning
School of Interactive Computing Georgia Institute of Technology Atlanta, GA 30332, USA
Learning Geometry-Aware Virtual Fixtures From Sparse Demonstrations
Sep 26, 2026Maximilian Mühlbauer, Marcella Piacentino, Raffaella Mancino +10Robotics Robot Teleoperation
German Aerospace Center (DLR), Robotics and Mechatronics Center (RMC), Münchener Str. 20, 82234 Weßling, Germany. · Department of Computer Engineering, Technical University of Munich, Friedrich-Ludwig-Bauer-Str. 3, Garching, Germany. · C.R.E.A.T.E. Consorzio di Ricerca per l’Energia, l’Automazione e le Tecnologie dell’Elettromagnetismo, Universit`a degli Studi di Napoli Federico II, Napoli, Italy +5
An Empirical Study on What Matters for Viewpoint-Generalizable Policies in Visual Imitation Learning
Sep 26, 2026Mino Nakura, Sriram Krishna, Yufei Wang +3Robot Imitation Learning Generalization in Robotic Manipulation
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Humanoid Badminton: Learning Dynamic Racket Skills from Limited Human Motion Data
Sep 25, 2026Jingzhi Cui, Zhexiong Wang, Bangjie Xu +9Robot Skill Learning Robotics
Tsinghua University · Hong Kong Embodied AI Lab · Zhejiang University +3
InternW0- $Δ$ : A World Action Model Bridging Predictive Dynamics and Actions with 20K+ Hours of Open Data
Sep 25, 2026Xingyu Miao, Zizun Li, Baole Fang +45World Models for Robotics Robot Foundation Models
Imp-ACT: Adaptive Impedance Control and Action Chunking with Transformers to Learn Contact-Rich Manipulation from Demonstrations
Sep 25, 2026Luca Zanetti, Doganay Sirintuna, Idil Ozdamar +3Contact-Rich Robotic Manipulation Impedance Control
Human-Robot Interfaces and Interaction Laboratory, Istituto Italiano di Tecnologia, Genoa, Italy · Safe AI Lab, Carnegie Mellon University
Enabling a Unified Cross-Domain Representation for Two-Finger Gripper Manipulation via Interaction-Centric Modeling
Sep 25, 2026Guanlin Li, Shifeng Bao, Yihan Zhao +6Robot Imitation Learning Cross-Embodiment Robot Learning
Key Laboratory of Data Engineering and Knowledge Engineering, MOE, and School of Information, Renmin University of China, China. · Zhipu AI, China. · Department of Computer Science and Technology, Tsinghua University, China.
Kintsugi-VLA: Turning Failed Robot Rollouts into Recovery Data through Interventional Recoverability
Sep 25, 2026Ivan Snegirev, Elizaveta Semenyakina, Dmitrii Maliukov +2Robot Failure Recovery Synthetic Data Generation for Robotics
Intelligent Space Robotics Laboratory, Skolkovo Institute of Science and Technology
FRAM: Trajectory-Guided Visual Feature Selection for Compact Language-Conditioned Robot Manipulation
Sep 25, 2026Hiroshi Ito, Hyogo Hiruma, Yoshiki Kanai +3Language-Conditioned Robot Manipulation Efficient VLA Models
Research & Development Group, Hitachi, Ltd., Ibaraki, 312-0034, Japan.
VisTacAlign: Co-Training Dexterous Policies on Tactile Human and Robot Demonstrations using Visual-Tactile Alignment
Sep 25, 2026Julien Poffet, Matthew Strong, Ankush Dhawan +5Robotic Manipulation Robot Imitation Learning
Department of Mechanical and Process Engineering (D-MAVT), ETH Zurich, Zurich, Switzerland. Work done as a visiting student at Stanford University. · Department of Computer Science, Stanford University, Stanford CA, USA. · Department of Mechanical Engineering, Stanford University, Stanford CA, USA. +2
PHASE: Compliance-Enabled Tactile Phase Retrieval for Few-Shot Insertion Learning
Sep 25, 2026Jeremy Siburian, Cristian C. Beltran-Hernandez, Tatsuya Matsushima +3Contact-Rich Robotic Manipulation Robot Imitation Learning
OMRON SINIC X Corporation, 5-24-5 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan · The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan

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AeroManip-VLA: Scalable Vision-Language-Action Learning for Aerial Manipulation with RL-Generated Demonstrations

Rui Huang, Yanlin Mu, Lidong Li, Yucong Wang+2

National University of Singapore · Beijing Institute of Technology

Synthetic Data Generation for Robotics Vision-Language-Action Models Robot Imitation Learning Robot Manipulation Benchmarks Aerial Manipulation

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Aerial manipulators extend robotic manipulation into 3D workspaces that are difficult for ground-based robots to access, creating new opportunities for general-purpose manipulation. However, extending Vision-Language-Action (VLA) models to aerial robots introduces distinct challenges due to the tight coupling between manipulation and flight, continuously changing observations, and safety-critical physical interactions. These challenges demand diverse training data and systematic policy evaluation, yet collecting demonstrations and evaluating policies directly on physical aerial platforms are costly, difficult to scale, and hard to repeat under controlled conditions. We present AeroManip-VLA, a scalable benchmark for aerial VLA data generation and policy evaluation. AeroManip-VLA provides a GPU-accelerated simulation framework with low-level payload-aware flight and manipulation control in massively parallel environments. Building on this framework, we combine reusable reinforcement learning policies with expert task rules to automatically generate demonstrations without human teleoperation across diverse objects, environments, and randomized initial conditions. The generated data include basic skills such as grasping and placing, as well as long-horizon tasks that require both navigation and manipulation. We further introduce automated event labeling and trajectory categorization to filter demonstrations. These mechanisms enable fine-grained analysis of task progress, behavioral outcomes, and safety-related failures. Finally, we evaluate a range of imitation learning and VLA baselines across different task settings, revealing their performance characteristics and failure modes. Together, AeroManip-VLA enables scalable aerial manipulation data generation, structured trajectory analysis, and systematic VLA evaluation in simulation prior to real-world deployment.