# Improving dual-arm assembly by master-slave compliance

## TL;DR

This paper investigates how compliance choices affect dual-arm assembly tasks and demonstrates how to learn optimal compliance parameters from human demonstrations to improve efficiency and accuracy.

## Contribution

It introduces a method to learn compliance parameters for dual-arm assembly from human demonstrations, enhancing task performance and ease of implementation.

## Key findings

- Learning compliance parameters reduces total joint motions.
- Dual-arm compliance shortens task completion time.
- Optimal compliance improves alignment success rate.

## Abstract

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.