Imitation Learning for Adaptive Control of a Virtual Soft Exoglove

TL;DR

This paper presents a reinforcement learning-based control system for a virtual soft exoglove that adapts to individual muscle deficits, improving hand manipulation in simulated rehabilitation scenarios.

Contribution

It introduces a novel simulation framework combining musculoskeletal modeling and reinforcement learning to customize wearable robotic assistance for neurological impairments.

Findings

Achieved 90.5% of original manipulation proficiency.

Successfully simulated muscle weakness and compensation.

Demonstrated effective adaptation of the exoglove controller.

Abstract

The use of wearable robots has been widely adopted in rehabilitation training for patients with hand motor impairments. However, the uniqueness of patients' muscle loss is often overlooked. Leveraging reinforcement learning and a biologically accurate musculoskeletal model in simulation, we propose a customized wearable robotic controller that is able to address specific muscle deficits and to provide compensation for hand-object manipulation tasks. Video data of a same subject performing human grasping tasks is used to train a manipulation model through learning from demonstration. This manipulation model is subsequently fine-tuned to perform object-specific interaction tasks. The muscle forces in the musculoskeletal manipulation model are then weakened to simulate neurological motor impairments, which are later compensated by the actuation of a virtual wearable robotics glove. Results shows that integrating the virtual wearable robotic glove provides shared assistance to support the hand manipulator with weakened muscle forces. The learned exoglove controller achieved an average of 90.5\% of the original manipulation proficiency.