Robust Impedance Control for Dexterous Interaction Using Fractal Impedance Controller with IK-Optimisation

TL;DR

This paper introduces a hierarchical control architecture with fractal impedance control and IK-optimization to enable robots to perform human-like motions robustly during unknown interactions, addressing limitations of previous methods.

Contribution

It presents a novel control framework combining fractal impedance control with IK-optimization for robust, human-like robot interaction and motion reproduction.

Findings

Reproduced trajectories preserve key characteristics of human motion.

The approach is robust against external perturbations.

Some movements are limited by hardware constraints.

Abstract

Robust dynamic interactions are required to move robots in daily environments alongside humans. Optimisation and learning methods have been used to mimic and reproduce human movements. However, they are often not robust and their generalisation is limited. This work proposed a hierarchical control architecture for robot manipulators and provided capabilities of reproducing human-like motions during unknown interaction dynamics. Our results show that the reproduced end-effector trajectories can preserve the main characteristics of the initial human motion recorded via a motion capture system, and are robust against external perturbations. The data indicate that some detailed movements are hard to reproduce due to the physical limits of the hardware that cannot reach the same velocity recorded in human movements. Nevertheless, these technical problems can be addressed by using better hardware and our proposed algorithms can still be applied to produce imitated motions.