# Learning Variable Compliance Control From a Few Demonstrations for Bimanual Robot with Haptic Feedback Teleoperation System

arXiv: https://arxiv.org/abs/2406.14990 · HTML: https://mlsanity.com/paper/2406.14990/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## Builds on (most cited first, 14 of 14)

- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [robosuite: A Modular Simulation Framework and Benchmark for Robot Learning](https://mlsanity.com/paper/2009.12293/) (2020)
- [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation](https://mlsanity.com/paper/1710.04615/) (2017)
- [RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitation](https://mlsanity.com/paper/1811.02790/) (2018)
- [GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators](https://mlsanity.com/paper/2309.13037/) (2023)
- [A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms](https://mlsanity.com/paper/1907.03146/) (2019)
- [Lipschitz Continuity in Model-based Reinforcement Learning](https://mlsanity.com/paper/1804.07193/) (2018)
- [Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2008.10224/) (2020)
- [Bi-ACT: Bilateral Control-Based Imitation Learning via Action Chunking with Transformer](https://mlsanity.com/paper/2401.17698/) (2024)
- [Baxter's Homunculus: Virtual Reality Spaces for Teleoperation in Manufacturing](https://mlsanity.com/paper/1703.01270/) (2017)
- [Variable impedance control and learning -- A review](https://mlsanity.com/paper/2010.06246/) (2020)
- [Imitation Learning for Nonprehensile Manipulation through Self-Supervised Learning Considering Motion Speed](https://mlsanity.com/paper/2206.10289/) (2022)
- [A Survey of Demonstration Learning](https://mlsanity.com/paper/2303.11191/) (2023)
- [An Open Tele-Impedance Framework to Generate Data for Contact-Rich Tasks in Robotic Manipulation](https://mlsanity.com/paper/2209.10486/) (2022)

## Built on by (most cited first, 13 of 13)

- [Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation](https://mlsanity.com/paper/2503.02881/) (2025)
- [TA-VLA: Elucidating the Design Space of Torque-aware Vision-Language-Action Models](https://mlsanity.com/paper/2509.07962/) (2025)
- [Sketch-to-Skill: Bootstrapping Robot Learning with Human Drawn Trajectory Sketches](https://mlsanity.com/paper/2503.11918/) (2025)
- [Learning Diffusion Policies from Demonstrations For Compliant Contact-rich Manipulation](https://mlsanity.com/paper/2410.19235/) (2024)
- [FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning](https://mlsanity.com/paper/2606.12406/) (2026)
- [ALPHA-α and Bi-ACT Are All You Need: Importance of Position and Force Information/Control for Imitation Learning of Unimanual and Bimanual Robotic Manipulation with Low-Cost System](https://mlsanity.com/paper/2411.09942/) (2024)
- [Improving Low-Cost Teleoperation: Augmenting GELLO with Force](https://mlsanity.com/paper/2507.13602/) (2025)
- [Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning](https://mlsanity.com/paper/2604.16850/) (2026)
- [VIDP: Variable Impedance Diffusion Policy for Compliant Robot Manipulation from Diverse Demonstrations](https://mlsanity.com/paper/2608.06210/) (2026)
- [Learning-based Cooperative Robotic Paper Wrapping: A Unified Control Policy with Residual Force Control](https://mlsanity.com/paper/2511.03181/) (2025)
- [Robustness of Robotic Manipulation: Foundations and Frontiers](https://mlsanity.com/paper/2606.31494/) (2026)
- [Continuous Manifold-Decomposed Impedance Retargeting for Contact-Rich Imitation Learning](https://mlsanity.com/paper/2609.15716/) (2026)
- [CompVLA: A Variable Compliance Vision-Language-Action Model for Contact-rich Manipulation](https://mlsanity.com/paper/2609.23614/) (2026)

## Abstract (arXiv)

Automating dexterous, contact-rich manipulation tasks using rigid robots is a significant challenge in robotics. Rigid robots, defined by their actuation through position commands, face issues of excessive contact forces due to their inability to adapt to contact with the environment, potentially causing damage. While compliance control schemes have been introduced to mitigate these issues by controlling forces via external sensors, they are hampered by the need for fine-tuning task-specific controller parameters. Learning from Demonstrations (LfD) offers an intuitive alternative, allowing robots to learn manipulations through observed actions. In this work, we introduce a novel system to enhance the teaching of dexterous, contact-rich manipulations to rigid robots. Our system is twofold: firstly, it incorporates a teleoperation interface utilizing Virtual Reality (VR) controllers, designed to provide an intuitive and cost-effective method for task demonstration with haptic feedback. Secondly, we present Comp-ACT (Compliance Control via Action Chunking with Transformers), a method that leverages the demonstrations to learn variable compliance control from a few demonstrations. Our methods have been validated across various complex contact-rich manipulation tasks using single-arm and bimanual robot setups in simulated and real-world environments, demonstrating the effectiveness of our system in teaching robots dexterous manipulations with enhanced adaptability and safety. Code available at: https://github.com/omron-sinicx/CompACT