Multifingered force-aware control for humanoid robots

TL;DR

This paper presents a force-aware control framework for humanoid robots with multi-fingered hands, utilizing tactile sensor data and force estimation to improve contact stability and force distribution during manipulation tasks.

Contribution

The authors introduce a novel control scheme that uses estimated forces from tactile sensors to adapt robot motion and force distribution, enhancing stability in multi-object manipulation.

Findings

Achieved 82.7% success rate in balancing tasks with five objects.

Demonstrated 80% accuracy in multi-object manipulation scenarios.

Validated the approach on a humanoid robot platform.

Abstract

In this paper, we address force-aware control and force distribution in robotic platforms with multi-fingered hands. Given a target goal and force estimates from tactile sensors, we design a controller that adapts the motion of the torso, arm, wrist, and fingers, redistributing forces to maintain stable contact with objects of varying mass distribution or unstable contacts. To estimate forces, we collect a dataset of tactile signals and ground-truth force measurements using five Xela magnetic sensors interacting with indenters, and train force estimators. We then introduce a model-based control scheme that minimizes the distance between the Center of Pressure (CoP) and the centroid of the fingertips contact polygon. Since our method relies on estimated forces rather than raw tactile signals, it has the potential to be applied to any sensor capable of force estimation. We validate our framework on a balancing task with five objects, achieving a $82.7\%$ success rate, and further evaluate it in multi-object scenarios, achieving $80\%$ accuracy. Code and data can be found here https://github.com/hsp-iit/multifingered-force-aware-control.