Towards Biosignals-Free Autonomous Prosthetic Hand Control via Imitation Learning

TL;DR

This paper presents a novel autonomous prosthetic hand control system that uses imitation learning and a camera, eliminating the need for users to generate myoelectric signals and reducing mental effort.

Contribution

The study introduces a camera-based, fully autonomous control system for prosthetic hands trained via imitation learning, enabling easier and more natural operation without myoelectric signals.

Findings

High success rates in grasping and releasing objects

Generalizes to new users and unseen objects

Requires only a few demonstration data points

Abstract

Limb loss affects millions globally, impairing physical function and reducing quality of life. Most traditional surface electromyographic (sEMG) and semi-autonomous methods require users to generate myoelectric signals for each control, imposing physically and mentally taxing demands. This study aims to develop a fully autonomous control system that enables a prosthetic hand to automatically grasp and release objects of various shapes using only a camera attached to the wrist. By placing the hand near an object, the system will automatically execute grasping actions with a proper grip force in response to the hand's movements and the environment. To release the object being grasped, just naturally place the object close to the table and the system will automatically open the hand. Such a system would provide individuals with limb loss with a very easy-to-use prosthetic control interface and greatly reduce mental effort while using. To achieve this goal, we developed a teleoperation system to collect human demonstration data for training the prosthetic hand control model using imitation learning, which mimics the prosthetic hand actions from human. Through training the model using only a few objects' data from one single participant, we have shown that the imitation learning algorithm can achieve high success rates, generalizing to more individuals and unseen objects with a variation of weights. The demonstrations are available at \href{https://sites.google.com/view/autonomous-prosthetic-hand}{https://sites.google.com/view/autonomous-prosthetic-hand}