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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Skills in Weights, Memory in Code: Hybrid Learning for Memory-Dependent Robot Manipulation
Aug 10, 2026Yunhao Zhao, Zhenyang Ni, Haoyang Chen +2Long-Horizon Robotic Manipulation Robot Skill Learning
Northwestern University · University of Minnesota · Stanford University
SAFE-CHEM: Uncertainty-Aware Policy Switching for Robust Robotic Chemistry
Aug 10, 2026Laura Jones, Shazil Shahzad, Ayesha Sana +1Robotic Control Robot Safety
School of Computer Science & Informatics, University of Liverpool, UK. · Department of Chemistry, University of Liverpool, UK.
High Fidelity Capture, Reconstruction, and Transfer of Human Demonstrations for Robot-Assisted Bathing
Aug 10, 2026Arjun S. Lakshmipathy, Jonathan P. King, Ethan Zuo +6Contact-Rich Robotic Manipulation Robotic Data Collection
School of Computer Science, Carnegie Mellon University · School of Health and Rehabilitation Sciences, University of Pittsburgh
Auditing Instruction-Trajectory Mismatches in Multimodal Robot Demonstrations
Aug 8, 2026Simon Holk, Ryosuke Takanami, Tatsuya Matsushima +4Robot Imitation Learning VLMs for Robotics
AI Robot Association (AIRoA), Tokyo, Japan
AutoIntervene: Calibrated Intervention for Action-Chunking Imitation Learning Policies
Aug 7, 2026Jinhe Tang, Weiming ZhiRobot Policy Learning Interactive Imitation Learning
School of Computer Science · Australian Center For Robotics · College of Connected Computing
C2Dex: Contact-Consistent Reconstruction and Retargeting for Dexterous Manipulation from Monocular Video
Aug 7, 2026Jie Ren, Zhehao Jiang, Yinhong Yang +9Contact-Rich Robotic Manipulation 3D Reconstruction
1Nanjing University, Nanjing, China. · 2China Mobile Research Institute, Beijing, China.
VIDP: Variable Impedance Diffusion Policy for Compliant Robot Manipulation from Diverse Demonstrations
Aug 6, 2026Hisham Khalil, Neil Fernandes, Thomas M. Kwok +2Contact-Rich Robotic Manipulation Diffusion Policy
Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada · Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada · School of Computer Science and the Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada
JoyAI-RA 0.5: Scaling Robot Manipulation Learning via Dual Action Alignment
Aug 6, 2026JoyAI-RA TeamRL for Robotics Latent Action Learning
Joy Future Academy, JD
SiMDex: Mining Similar Egocentric Videos for Cross-Embodiment Dexterous Manipulation
Aug 4, 2026Nie Lin, Takehiko Ohkawa, Sijin Chen +10Cross-Embodiment Robot Learning Robotic Manipulation
The University of Tokyo · The University of Hong Kong · ByteDance Seed +2
Bimanual Manipulation Within an 8 GB Budget: Zero-Copy Sensing and Quantized ACT on an Entry-Level Jetson
Aug 4, 2026Ekansh Singh, Eva Samuel, Alessandra Reneau +2Bimanual Robotic Manipulation Robot Imitation Learning
Georgia Tech Research Institute (GTRI), Aerospace, Transportation and Advanced Systems Laboratory (ATAS), Atlanta, GA, USA
Shooting for Contact: Contact-Implicit Multiple Shooting for Dynamic Motion Retargeting
Aug 4, 2026Sergio A. Esteban, Jason H. K. Siu, Derrick Mach +4Trajectory Optimization Multibody Dynamics
Department of Mechanical and Civil Engineering, California Institute of Technology, Pasadena, CA, USA. · School of Computing, DePaul University, Chicago, IL, USA.
Why Does Action Chunking Improve Behavioral Cloning Performance in Robotic Control?
Aug 3, 2026Filippo Lazzati, Kyle Stachowicz, William Chen +3Ensemble Learning Robot Imitation Learning
Politecnico di Milano · UC Berkeley
StableMimic: Smooth Human-Like Recovery for Humanoid Motion Tracking - Learning Beyond the Tracking Distribution for Structured Post-Fall Behavior
Aug 3, 2026Weihao Wu, Ming Huang, Ruofei Liu +3Robot Failure Recovery Robot Imitation Learning
Southern University of Science and Technology (SUSTech), Shenzhen, China.
TWINS: A Tactile Wearable Isomorphic Arm Networked System for Contact-Rich Manipulation Learning
Aug 3, 2026Takahide Kitamura, Masaki Murooka, Natsuki Yamanobe +1Bimanual Robotic Manipulation Contact-Rich Robotic Manipulation
National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26 Aomi, Koto-ku, 135-0064, Tokyo, Japan.
AffordTrajDP: Dynamic Affordance-Guided Visuomotor Policy Learning for Robotic Manipulation
Aug 3, 2026Gaoyuan Wu, Ziyu Shan, Haoyang Du +2Robot Skill Learning Generalization in Robotic Manipulation
1Nanyang Technological University, Singapore
Perception-and-action system for humanoid robot task execution in construction
Aug 3, 2026Yanxi Liu, Yizhi LiuRobotic Construction Motion Retargeting
Department of Civil and Environmental Engineering, Syracuse University, 158 Link Hall, Syracuse, NY 13244, USA · Department of Civil and Environmental Engineering, Syracuse University, 151J Link Hall, Syracuse, NY 13244, USA
CAAT: Contact-Aware Attention Scaling and Tactile Masking for Data-Efficient Contact-Rich Manipulation
Aug 2, 2026Jiaming Jiang, Yuzhe Huang, Hao Liang +7Contact-Rich Robotic Manipulation Visuo-Tactile Representation Learning
ShanghaiTech University · Beijing Institute for General Artificial Intelligence · Beihang University +2
Self Supervised Learning from Automatically Generated Demonstrations for Visual Robotic Manipulation
Aug 1, 2026Andres Rivas, Anselmo R. Cukla, Rodrigo S. Guerra +2Robotic Grasping Robot Skill Learning
Robotics and AI Lab, Technological University of Uruguay, Rivera, Uruguay · Center of Technology, Federal University of Santa Maria, Santa Maria, Brazil · Computer Science Center, Federal University of Rio Grande, Rio Grande, Brazil
DexMani: Human-Derived Manipulability Guidance for Dexterous Rotation
Aug 1, 2026Xiaoyang Chen, Shengcheng Luo, Haoran Guo +4Dexterous Manipulation Robot Skill Learning
Shanghai Jiao Tong University · ShanghaiTech University · Beijing Institute for General Artificial Intelligence (BIGAI) +1
RayViT: Ray-Conditioned Visual Representations for Viewpoint-Robust Imitation Learning
Jul 31, 2026Qian Wang, Longrui Chen, Peiran Sun +8Visual Representation Learning Robot Skill Learning
Karlsruhe Institute of Technology, Germany · University of Leeds, United Kingdom
Temporal Policy: History-Initialized Action Generation for Robotic Learning from Demonstration
Jul 31, 2026Dylan Miller, Martin JagersandFlow Matching Robot Policy Learning
Department of Computing Science, University of Alberta, Canada
TRACT: Temporally Routed Action Chunks with Chronological Phase Authority for Contact-Rich Manipulation
Jul 31, 2026Jiahao Liu, Kento Kawaharazuka, Tasuku Makabe +1Robotic Control Robot Skill Learning
Department of Mechano-Informatics Graduate School of Information Science and Technology, The University of Tokyo Tokyo, Japan
Mirror Learning
Jul 30, 2026Yunpeng Liu, Matthew Niedoba, Oluwanifemi A. Adekanye +4Imitation Learning Robot Imitation Learning
University of British Columbia · 2Inverted AI · 3Amii
Static In, Dynamic Out: Counterfactual Action Augmentation for Moving Object Manipulation
Jul 30, 2026Woo Chul Shin, Zhenyang Chen, Alfred Cueva +5Robot Policy Adaptation Visuomotor Policy Learning
Georgia Institute of Technology
Failure Detection for Surgical Robot Imitation Policies via Flow-Matching World Modeling
Jul 29, 2026Zhefeng Huang, Yilin Cai, Ankit Patel +3Robot Failure Detection World Models
Department of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
It's Not Just More Demos: Counterfactual Action Sensitivity Coverage for Data-Efficient Robust Robot Imitation
Jul 29, 2026Giovanni D'urso, Kaushik Roy, Nicholas Lawrance +1Visuomotor Policy Learning Imitation Learning
CSIRO Robotics, CSIRO, Australia
RLMM-Flow: A Flow-based Mobile Manipulation Framework with Latent-Space Reinforcement Learning
Jul 29, 2026Shuhang Wang, Ziming Li, Hui ChengReinforcement Learning Mobile Manipulation
MoMo: Dial Motion Mode in Robot Manipulation with Spatiotemporal Action Tokenization
Jul 28, 2026Yuhan Hu, Hugues Thomas, Peide Huang +3Robot Skill Learning Generalization in Robotic Manipulation
Apple
S2A2: Audio-Visual Imitation Learning for Manipulation Tasks Using Acoustic Spatial Information
Jul 28, 2026Kaneyoshi Hiratsuka, Benjamin Yen, Ryosuke KojimaImitation Learning Robotic Manipulation
Kyoto University · RIKEN, Institute of Science Tokyo · Kyoto University, RIKEN
HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone
Jul 28, 2026Simple AI, :, Yuteng Wei +16Robot Policy Learning Robotic Data Collection
Simple AI

Aug 10, 2026J/K move · Enter open · S save

Skills in Weights, Memory in Code: Hybrid Learning for Memory-Dependent Robot Manipulation

Yunhao Zhao, Zhenyang Ni, Haoyang Chen, Ruohan Zhang+1

Northwestern University · University of Minnesota · Stanford University

Long-Horizon Robotic Manipulation Robot Skill Learning Robotic Manipulation Robot Imitation Learning VLMs for Robotics Memory-Augmented Agents

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Modern vision-language-action (VLA) policies have acquired broad manipulation skills, but typically generate each action chunk from the current observation or a short fixed-length history. However, real-world manipulation is often non-Markovian, requiring robots to retain and reason over task-relevant information from long-horizon interaction histories to determine the next action. To address this challenge, we propose HyMeS, a hybrid learning framework that leverages the reasoning and memory-management capabilities of coding agents to steer a Markovian VLA for memory-dependent manipulation. Specifically, HyMeS learns low-level motor skills through gradient-based imitation learning, while a coding agent acquires high-level memory-management strategies through heuristic learning by iteratively updating an executable heuristic system from rollout feedback. Furthermore, we close the loop between steering and execution through multimodal stage-completion verification, which updates memory using proprioceptive signals and multi-frame VLM judgments. Compared with end-to-end memory-augmented VLAs, HyMeS requires demonstrations only for reusable motor skills rather than for every history-dependent task configuration, enabling data-efficient compositional generalization. On RoboMemArena, HyMeS improves mean cumulative success from 52.5% to 66.2% and mean task success from 41.3% to 60.1% over pi0.5, while outperforming PrediMem by 4.5 points in cumulative success and 14.5 points in task success.