# Improving dual-arm assembly by master-slave compliance

arXiv: https://arxiv.org/abs/1902.07007 · HTML: https://mlsanity.com/paper/1902.07007/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2019.

## Built on by (most cited first, 4 of 4)

- [Learning to Centralize Dual-Arm Assembly](https://mlsanity.com/paper/2110.04003/) (2021)
- [SkillVLA: Tackling Combinatorial Diversity in Dual-Arm Manipulation via Skill Reuse](https://mlsanity.com/paper/2603.03836/) (2026)
- [DA-VIL: Adaptive Dual-Arm Manipulation with Reinforcement Learning and Variable Impedance Control](https://mlsanity.com/paper/2410.19712/) (2024)
- [Imitation Learning Based on Bilateral Control for Human-Robot Cooperation](https://mlsanity.com/paper/1909.13018/) (2019)

## Abstract (arXiv)

In this paper we show how different choices regarding compliance affect a dual-arm assembly task. In addition, we present how the compliance parameters can be learned from a human demonstration. Compliant motions can be used in assembly tasks to mitigate pose errors originating from, for example, inaccurate grasping. We present analytical background and accompanying experimental results on how to choose the center of compliance to enhance the convergence region of an alignment task. Then we present the possible ways of choosing the compliant axes for accomplishing alignment in a scenario where orientation error is present. We show that an earlier presented Learning from Demonstration method can be used to learn motion and compliance parameters of an impedance controller for both manipulators. The learning requires a human demonstration with a single teleoperated manipulator only, easing the execution of demonstration and enabling usage of manipulators at difficult locations as well. Finally, we experimentally verify our claim that having both manipulators compliant in both rotation and translation can accomplish the alignment task with less total joint motions and in shorter time than moving one manipulator only. In addition, we show that the learning method produces the parameters that achieve the best results in our experiments.