Learning Human Behaviors for Robot-Assisted Dressing

TL;DR

This paper uses reinforcement learning to develop models of human behavior for robot-assisted dressing, enabling robots to anticipate and assist with dressing motions in simulated environments.

Contribution

It introduces a reinforcement learning framework to model human arm movements during dressing assistance, focusing on what humans are capable of doing rather than typical behaviors.

Findings

Successfully trained models for three dressing strategies

Demonstrated the robot's ability to predict human arm movements

Used physics-based simulation for realistic modeling

Abstract

We investigate robotic assistants for dressing that can anticipate the motion of the person who is being helped. To this end, we use reinforcement learning to create models of human behavior during assistance with dressing. To explore this kind of interaction, we assume that the robot presents an open sleeve of a hospital gown to a person, and that the person moves their arm into the sleeve. The controller that models the person's behavior is given the position of the end of the sleeve and information about contact between the person's hand and the fabric of the gown. We simulate this system with a human torso model that has realistic joint ranges, a simple robot gripper, and a physics-based cloth model for the gown. Through reinforcement learning (specifically the TRPO algorithm) the system creates a model of human behavior that is capable of placing the arm into the sleeve. We aim to model what humans are capable of doing, rather than what they typically do. We demonstrate successfully trained human behaviors for three robot-assisted dressing strategies: 1) the robot gripper holds the sleeve motionless, 2) the gripper moves the sleeve linearly towards the person from the front, and 3) the gripper moves the sleeve linearly from the side.