Naturalistic Robot Arm Trajectory Generation via Representation Learning

TL;DR

This paper presents a self-supervised imitation learning approach using a spatio-temporal graph neural network to generate naturalistic and functional robot arm trajectories for assistive drinking tasks, based on human motion data.

Contribution

It introduces a novel autoregressive graph neural network model for learning from diverse human motion data to produce human-like robot trajectories.

Findings

Successfully generates natural drinking motion trajectories for a UR5e robot.

Learns from wearable IMU sensor data of human arm movements.

Demonstrates improved naturalness and functionality in robot motion.

Abstract

The integration of manipulator robots in household environments suggests a need for more predictable and human-like robot motion. This holds especially true for wheelchair-mounted assistive robots that can support the independence of people with paralysis. One method of generating naturalistic motion trajectories is via the imitation of human demonstrators. This paper explores a self-supervised imitation learning method using an autoregressive spatio-temporal graph neural network for an assistive drinking task. We address learning from diverse human motion trajectory data that were captured via wearable IMU sensors on a human arm as the action-free task demonstrations. Observed arm motion data from several participants is used to generate natural and functional drinking motion trajectories for a UR5e robot arm.