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

arXiv: https://arxiv.org/abs/2310.10509 · HTML: https://mlsanity.com/paper/2310.10509/

## Key facts

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

## Built on by (most cited first, 21 of 21)

- [ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation](https://mlsanity.com/paper/2505.22159/) (2025)
- [ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning](https://mlsanity.com/paper/2503.21860/) (2025)
- [TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction](https://mlsanity.com/paper/2405.10315/) (2024)
- [Sparse Diffusion Policy: A Sparse, Reusable, and Flexible Policy for Robot Learning](https://mlsanity.com/paper/2407.01531/) (2024)
- [Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks](https://mlsanity.com/paper/2312.06876/) (2023)
- [Efficient Residual Learning with Mixture-of-Experts for Universal Dexterous Grasping](https://mlsanity.com/paper/2410.02475/) (2024)
- [DyTact: Capturing Dynamic Contacts in Hand-Object Manipulation](https://mlsanity.com/paper/2506.03103/) (2025)
- [Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks](https://mlsanity.com/paper/2503.21989/) (2025)
- [Bootstrapping Imitation Learning for Long-horizon Manipulation via Hierarchical Data Collection Space](https://mlsanity.com/paper/2505.17389/) (2025)
- [TacPAC: Tactile Prediction and Real-Time Action Correction in World-Action Models for Contact-Rich Manipulation](https://mlsanity.com/paper/2609.05266/) (2026)
- [Leveraging Extrinsic Dexterity for Occluded Grasping on Grasp Constraining Walls](https://mlsanity.com/paper/2507.14721/) (2025)
- [Data-Efficient Multitask DAgger](https://mlsanity.com/paper/2509.25466/) (2025)
- [How to Peel with a Knife: Aligning Fine-Grained Manipulation with Human Preference](https://mlsanity.com/paper/2603.03280/) (2026)
- [FurnitureVLA: Learning Long-Horizon Bimanual Furniture Assembly with Vision-Language-Action Model](https://mlsanity.com/paper/2607.01212/) (2026)
- [Watch Less, Feel More: Sim-to-Real RL for Generalizable Articulated Object Manipulation via Motion Adaptation and Impedance Control](https://mlsanity.com/paper/2502.14457/) (2025)
- [Robot Policy Transfer with Online Demonstrations: An Active Reinforcement Learning Approach](https://mlsanity.com/paper/2503.12993/) (2025)
- [Learning Pivoting Manipulation with Force and Vision Feedback Using Optimization-based Demonstrations](https://mlsanity.com/paper/2508.01082/) (2025)
- [Learning Hybrid-Control Policies for High-Precision In-Contact Manipulation Under Uncertainty](https://mlsanity.com/paper/2604.19677/) (2026)
- [V2P-Manip: Learning Dexterous Manipulation from Monocular Human Videos](https://mlsanity.com/paper/2606.16436/) (2026)
- [Force-Aware Reinforcement Learning with Hybrid Sensorless Force Estimation for Wheeled-Legged Loco-Manipulation](https://mlsanity.com/paper/2609.13779/) (2026)
- [Chronos Policy: Learning Robot Policies on Physical Time](https://mlsanity.com/paper/2610.13929/) (2026)

## 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.