# A System for Imitation Learning of Contact-Rich Bimanual Manipulation Policies

arXiv: https://arxiv.org/abs/2208.00596 · HTML: https://mlsanity.com/paper/2208.00596/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 1 Aug 2022.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Builds on (most cited first, 4 of 4)

- [A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms](https://mlsanity.com/paper/1907.03146/) (2019)
- [The Ensemble Kalman Filter: A Signal Processing Perspective](https://mlsanity.com/paper/1702.08061/) (2017)
- [A Certified-Complete Bimanual Manipulation Planner](https://mlsanity.com/paper/1705.02573/) (2017)
- [Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks](https://mlsanity.com/paper/1908.04955/) (2019)

## Built on by (most cited first, 26 of 26)

- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation](https://mlsanity.com/paper/2410.07864/) (2024)
- [Twisting Lids Off with Two Hands](https://mlsanity.com/paper/2403.02338/) (2024)
- [Stabilize to Act: Learning to Coordinate for Bimanual Manipulation](https://mlsanity.com/paper/2309.01087/) (2023)
- [Bi-VLA: Vision-Language-Action Model-Based System for Bimanual Robotic Dexterous Manipulations](https://mlsanity.com/paper/2405.06039/) (2024)
- [VoxAct-B: Voxel-Based Acting and Stabilizing Policy for Bimanual Manipulation](https://mlsanity.com/paper/2407.04152/) (2024)
- [DAG-Plan: Generating Directed Acyclic Dependency Graphs for Dual-Arm Cooperative Planning](https://mlsanity.com/paper/2406.09953/) (2024)
- [A Survey on Imitation Learning for Contact-Rich Tasks in Robotics](https://mlsanity.com/paper/2506.13498/) (2025)
- [Large Language Models for Orchestrating Bimanual Robots](https://mlsanity.com/paper/2404.02018/) (2024)
- [OpenVR: Teleoperation for Manipulation](https://mlsanity.com/paper/2305.09765/) (2023)
- [A Comparison of Imitation Learning Algorithms for Bimanual Manipulation](https://mlsanity.com/paper/2408.06536/) (2024)
- [LEMMA: Learning Language-Conditioned Multi-Robot Manipulation](https://mlsanity.com/paper/2308.00937/) (2023)
- [FILIC: Dual-Loop Force-Guided Imitation Learning with Impedance Torque Control for Contact-Rich Manipulation Tasks](https://mlsanity.com/paper/2509.17053/) (2025)
- [Lemmanaid: Neuro-Symbolic Lemma Conjecturing](https://mlsanity.com/paper/2504.04942/) (2025)
- [ARCADE: Scalable Demonstration Collection and Generation via Augmented Reality for Imitation Learning](https://mlsanity.com/paper/2410.15994/) (2024)
- [Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning](https://mlsanity.com/paper/2303.14870/) (2023)
- [Explaining Agent Behavior with Large Language Models](https://mlsanity.com/paper/2309.10346/) (2023)
- [VTAO-BiManip: Masked Visual-Tactile-Action Pre-training with Object Understanding for Bimanual Dexterous Manipulation](https://mlsanity.com/paper/2501.03606/) (2025)
- [ROPA: Synthetic Robot Pose Generation for RGB-D Bimanual Data Augmentation](https://mlsanity.com/paper/2509.19454/) (2025)
- [Zero-Shot Transfer of Haptics-Based Object Insertion Policies](https://mlsanity.com/paper/2301.12587/) (2023)
- [Towards a Generalizable Bimanual Foundation Policy via Flow-based Video Prediction](https://mlsanity.com/paper/2505.24156/) (2025)
- [Robust Bimanual Vision-Language-Action Models via Embarrassingly Simple Modality Masking](https://mlsanity.com/paper/2608.22419/) (2026)
- [Augmented Reality Demonstrations for Scalable Robot Imitation Learning](https://mlsanity.com/paper/2403.13910/) (2024)
- [Frequency-aware decomposition learning for sensorless wrench estimation in vibration-rich robotic contact](https://mlsanity.com/paper/2604.12905/) (2026)
- [Phase-Conditioned Imitation Learning with Autonomous Failure Recovery for Robust Deformable Object Manipulation](https://mlsanity.com/paper/2605.29407/) (2026)
- [A Hierarchical Approach to Imitation Learning for Manipulation Tasks Requiring Time Varying Forces](https://mlsanity.com/paper/2608.03103/) (2026)

## Abstract (arXiv)

In this paper, we discuss a framework for teaching bimanual manipulation tasks by imitation. To this end, we present a system and algorithms for learning compliant and contact-rich robot behavior from human demonstrations. The presented system combines insights from admittance control and machine learning to extract control policies that can (a) recover from and adapt to a variety of disturbances in time and space, while also (b) effectively leveraging physical contact with the environment. We demonstrate the effectiveness of our approach using a real-world insertion task involving multiple simultaneous contacts between a manipulated object and insertion pegs. We also investigate efficient means of collecting training data for such bimanual settings. To this end, we conduct a human-subject study and analyze the effort and mental demand as reported by the users. Our experiments show that, while harder to provide, the additional force/torque information available in teleoperated demonstrations is crucial for phase estimation and task success. Ultimately, force/torque data substantially improves manipulation robustness, resulting in a 90% success rate in a multipoint insertion task. Code and videos can be found at https://bimanualmanipulation.com/