Configuration Space Metrics

TL;DR

This paper investigates how different configuration space metrics, including non-Euclidean ones, influence robot motion solutions and user preferences, revealing that task-specific metrics can produce more natural and preferred behaviors.

Contribution

It analyzes the impact of various configuration space metrics on robot behavior and user preferences, highlighting the benefits of task-specific, non-Euclidean metrics.

Findings

Euclidean metrics work well for some tasks

Penalizing elbow motion improves naturalness in others

Correlating joints can align robot motion with user preferences

Abstract

When robot manipulators decide how to reach for an object, hand it over, or obey some task constraint, they implicitly assume a Euclidean distance metric in their configuration space. Their notion of what makes a configuration closer or further is dictated by this assumption. But different distance metrics will lead to different solutions. What is efficient under a Euclidean metric might not necessarily look the most efficient or natural to a person observing the robot. In this paper, we analyze the effect of the metric on robot behavior, examining both Euclidean, as well as non-Euclidean metrics -- metrics that make certain joints cheaper, or that correlate different joints. Our user data suggests that tasks on a 3DOF arm and the Jaco 7DOF arm can typically be grouped into ones where a Euclidean metric works well, and tasks where that is no longer the case: there, surprisingly, penalizing elbow motion (and sometimes correlating the shoulder and wrist) leads to solutions that are more aligned with what users prefer.