mlsanityData to 14 Oct 2026

Efficient Sim-to-real Transfer of Contact-Rich Manipulation Skills with Online Admittance Residual Learning

arXiv 2310.10509 ↗First posted 16 Oct 2023cs.ROcited by 21

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Abstract (arXiv)

Learning contact-rich manipulation skills is essential. Such skills require the robots to interact with the environment with feasible manipulation trajectories and suitable compliance control parameters to enable safe and stable contact. However, learning these skills is challenging due to data inefficiency in the real world and the sim-to-real gap in simulation. In this paper, we introduce a hybrid offline-online framework to learn robust manipulation skills. We employ model-free reinforcement learning for the offline phase to obtain the robot motion and compliance control parameters in simulation RVwith domain randomization. Subsequently, in the online phase, we learn the residual of the compliance control parameters to maximize robot performance-related criteria with force sensor measurements in real time. To demonstrate the effectiveness and robustness of our approach, we provide comparative results against existing methods for assembly, pivoting, and screwing tasks.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Oct 2023.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 10; 2026: 7.

21 papers.

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  1. 2025ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulationcited by 91
  2. 2025ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learningcited by 66
  3. 2024TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correctioncited by 59
  4. 2024Sparse Diffusion Policy: A Sparse, Reusable, and Flexible Policy for Robot Learningcited by 57
  5. 2023Interactive Planning Using Large Language Models for Partially Observable Robotics Taskscited by 27
  6. 2024Efficient Residual Learning with Mixture-of-Experts for Universal Dexterous Graspingcited by 10
  7. 2025DyTact: Capturing Dynamic Contacts in Hand-Object Manipulationcited by 8
  8. 2025Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Taskscited by 3
  9. 2025Bootstrapping Imitation Learning for Long-horizon Manipulation via Hierarchical Data Collection Spacecited by 3
  10. 2026TacPAC: Tactile Prediction and Real-Time Action Correction in World-Action Models for Contact-Rich Manipulationcited by 2
  11. 2025Leveraging Extrinsic Dexterity for Occluded Grasping on Grasp Constraining Wallscited by 1
  12. 2025Data-Efficient Multitask DAggercited by 1
9 more
  1. 2026How to Peel with a Knife: Aligning Fine-Grained Manipulation with Human Preferencecited by 1
  2. 2026FurnitureVLA: Learning Long-Horizon Bimanual Furniture Assembly with Vision-Language-Action Modelcited by 1
  3. 2025Watch Less, Feel More: Sim-to-Real RL for Generalizable Articulated Object Manipulation via Motion Adaptation and Impedance Controlcited by 0
  4. 2025Robot Policy Transfer with Online Demonstrations: An Active Reinforcement Learning Approachcited by 0
  5. 2025Learning Pivoting Manipulation with Force and Vision Feedback Using Optimization-based Demonstrationscited by 0
  6. 2026Learning Hybrid-Control Policies for High-Precision In-Contact Manipulation Under Uncertaintycited by 0
  7. 2026V2P-Manip: Learning Dexterous Manipulation from Monocular Human Videoscited by 0
  8. 2026Force-Aware Reinforcement Learning with Hybrid Sensorless Force Estimation for Wheeled-Legged Loco-Manipulationcited by 0
  9. 2026Chronos Policy: Learning Robot Policies on Physical Timecited by 0