Joint action understanding improves robot-to-human object handover

TLDR

Trustworthy human‑assistive robots require safety, predictability, and usefulness, making their development a multidisciplinary challenge. This study aims to show that incorporating joint action understanding from human‑human interactions improves robot‑to‑human handover success. The authors employ a two‑layer system: a physical layer with an HMM to decide release, and a cognitive layer that incorporates eye gaze and head orientation to model human behavior. Integrating eye gaze and head orientation cues significantly increases handover success, yielding a more robust and safer system.

Abstract

The development of trustworthy human-assistive robots is a challenge that goes beyond the traditional boundaries of engineering. Essential components of trustworthiness are safety, predictability and usefulness. In this paper we demonstrate that the integration of joint action understanding from human-human interaction into the human-robot context can significantly improve the success rate of robot-to-human object handover tasks. We take a two layer approach. The first layer handles the physical aspects of the handover. The robot's decision to release the object is informed by a Hidden Markov Model that estimates the state of the handover. Inspired by human-human handover observations, we then introduce a higher-level cognitive layer that models behaviour characteristic for a human user in a handover situation. In particular, we focus on the inclusion of eye gaze / head orientation into the robot's decision making. Our results demonstrate that by integrating these non-verbal cues the success rate of robot-to-human handovers can be significantly improved, resulting in a more robust and therefore safer system.