An Experimental Validation and Comparison of Reaching Motion Models for Unconstrained Handovers: Towards Generating Humanlike Motions for Human-Robot Handovers

TL;DR

This study experimentally compares different motion models for human reaching in unconstrained handovers, finding the Conic model best fits human motions and revealing a split between elliptical and hyperbolic types, aiding humanlike robot motion generation.

Contribution

It provides the first experimental validation of motion models for human handover reaches and identifies the Conic model as the most accurate for unconstrained motions.

Findings

Conic model best fits human handover reaching motions.

Unconstrained reaches show a split between elliptical and hyperbolic conic types.

Results will guide more humanlike robot motion generation.

Abstract

The Minimum Jerk motion model has long been cited in literature for human point-to-point reaching motions in single-person tasks. While it has been demonstrated that applying minimum-jerk-like trajectories to robot reaching motions in the joint action task of human-robot handovers allows a robot giver to be perceived as more careful, safe, and skilled, it has not been verified whether human reaching motions in handovers follow the Minimum Jerk model. To experimentally test and verify motion models for human reaches in handovers, we examined human reaching motions in unconstrained handovers (where the person is allowed to move their whole body) and fitted against 1) the Minimum Jerk model, 2) its variation, the Decoupled Minimum Jerk model, and 3) the recently proposed Elliptical (Conic) model. Results showed that Conic model fits unconstrained human handover reaching motions best. Furthermore, we discovered that unlike constrained, single-person reaching motions, which have been found to be elliptical, there is a split between elliptical and hyperbolic conic types. We expect our results will help guide generation of more humanlike reaching motions for human-robot handover tasks.