Paper Info
Title
From human action understanding to robot action execution: how the physical properties of handled objects modulate non-verbal cues
Abstract
Humans manage to communicate action intentions in a non-verbal way, through body posture and movement. We start from this observation to investigate how a robot can decode a human's non-verbal cues during the manipulation of an object, with specific physical properties, to learn the adequate level of “carefulness” to use when handling that object. We construct dynamical models of the human behaviour using a human-to-human handover dataset consisting of 3 different cups with different levels of fillings. We then included these models into the design of an online classifier that identifies the type of action, based on the human wrist movement. We close the loop from action understanding to robot action execution with an adaptive and robust controller based on the learned classifier, and evaluate the entire pipeline on a collaborative task with a 7-DOF manipulator. Our results show that it is possible to correctly understand the “carefulness” behaviour of humans during object manipulation, even in the pick and place scenario, that was not part of the training set.
Year
DOI
Venue
2020
10.1109/ICDL-EpiRob48136.2020.9278084
2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)
Keywords
DocType
ISSN
robust controller,physical properties,body posture,nonverbal cues,human action,object manipulation,manipulator,online classifier,robot action execution,action understanding,human wrist movement,human-to-human handover dataset,human behaviour,dynamical models
Conference
2161-9484
ISBN
Citations 
PageRank 
978-1-7281-7320-7
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Nuno Ferreira Duarte101.01
Konstantinos Chatzilygeroudis200.34
Santos-Victor, J.31747169.53
Aude Billard43316254.98