Paper Info
Title
Joint action understanding improves robot-to-human object handover
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.
Year
DOI
Venue
2013
10.1109/IROS.2013.6697021
Intelligent Robots and Systems
Keywords
Field
DocType
decision making,hidden Markov models,human-robot interaction,eye gaze,head orientation,hidden Markov model,human-human handover observations,human-human interaction,joint action understanding,robot decision making,robot-to-human object handover,trustworthy human-assistive robots
Computer vision,Verification and validation,Joint attention,Computer science,SAFER,Eye tracking,Artificial intelligence,Robot,Hidden Markov model,Handover,Human–robot interaction
Conference
ISSN
Citations 
PageRank 
2153-0858
8
0.58
References 
Authors
0
5
Name
Order
Citations
PageRank
Elena Corina Grigore1164.50
Kerstin Eder223226.56
Anthony G. Pipe325539.08
Chris Melhuish474787.61
Ute Leonards5131.35