HRI 2019 — 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

The Effects of Proactive Release Behaviors During Human-Robot Handovers

Zhao Han and Holly A. Yanco

28% Overall Acceptance Rate
4 Baxter robot released the object and the participant took it scaled
4 Baxter robot released the object and the participant took it
News
  • Oct 1, 2018

    Our paper is accepted to the top tier ACM/IEEE Human-Robot Interaction Conference (HRI '19)! The study is ready to reproduce with a Baxter robot. Code and environment setup are open sourced on GitHub.

Abstract

Most research on human-robot handovers focuses on how the robot should approach human receivers and notify them of the readiness to take an object; few studies have investigated the effects of different release behaviors.

Not releasing an object when a person desires to take it breaks handover fluency and creates a bad handover experience.

In this paper, we investigate the effects of different release behaviors. Specifically, we study the benefits of a proactive release, during which the robot actively detects a human grasp effort pattern.

In a 36-participant user study, results suggest proactive release is more efficient than rigid release (which only releases when the robot is fully stopped) and passive release (the robot detects pulling by checking if a threshold value is reached).

Subjectively, the overall handover experience is improved: the proactive release is significantly better in terms of handover fluency and ease-of-taking.

Video

Figures

Three human robot handover releases rigid passive and proactive release
Three human-robot handover releases – rigid, passive, and proactive release.
The three release policies are illustrated at the top. Proactive release is more efficient and preferred.
Baxter is ready to hand the object
Baxter is ready to hand the object.
Human grasp effort is detected and the participant took the object with ease
Human grasp effort is detected and the participant took the object with ease.

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