Learning to Control Complex Robots Using High-Dimensional Interfaces: Preliminary Insights

TL;DR

This paper investigates using high-dimensional motion sensor data from limited upper-body movements to control a robotic arm, highlighting the potential for robot intelligence to enhance learning and control in assistive robotics.

Contribution

It presents preliminary insights into leveraging robot intelligence to interpret complex human motion signals for improved control of assistive robotic arms.

Findings

Detection of inconsistencies in control dimension usage

Identification of asymmetries in control signals

Monitoring user learning progress

Abstract

Human body motions can be captured as a high-dimensional continuous signal using motion sensor technologies. The resulting data can be surprisingly rich in information, even when captured from persons with limited mobility. In this work, we explore the use of limited upper-body motions, captured via motion sensors, as inputs to control a 7 degree-of-freedom assistive robotic arm. It is possible that even dense sensor signals lack the salient information and independence necessary for reliable high-dimensional robot control. As the human learns over time in the context of this limitation, intelligence on the robot can be leveraged to better identify key learning challenges, provide useful feedback, and support individuals until the challenges are managed. In this short paper, we examine two uninjured participants' data from an ongoing study, to extract preliminary results and share insights. We observe opportunities for robot intelligence to step in, including the identification of inconsistencies in time spent across all control dimensions, asymmetries in individual control dimensions, and user progress in learning. Machine reasoning about these situations may facilitate novel interface learning in the future.