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Title: Classification of Co-Manipulation Modus with Human-Human Teams for Future Application to Human-Robot Systems
Despite the existence of robots that can physically lift heavy loads, robots that can collaborate with people to move heavy objects are not readily available. This article makes progress toward effective human-robot co-manipulation by studying 30 human-human dyads that collaboratively manipulated an object weighing\(27 \mathrm{kg}\)without being co-located (i.e., participants were at either end of the extended object). Participants maneuvered around different obstacles with the object while exhibiting one of four modi–the manner or objective with which a team moves an object together–at any given time. Using force and motion signals to classify modus or behavior was the primary objective of this work. Our results showed that two of the originally proposed modi were very similar, such that one could effectively be removed while still spanning the space of common behaviors during our co-manipulation tasks. The three modi used in classification werequickly,smoothlyandavoiding obstacles. Using a deep convolutional neural network (CNN), we classified three modi with up to 89% accuracy from a validation set. The capability to detect or classify modus during co-manipulation has the potential to greatly improve human-robot performance by helping to define appropriate robot behavior or controller parameters depending on the objective or modus of the team.  more » « less
Award ID(s):
2024792
PAR ID:
10561969
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
ACM Transactions on Human-Robot Interaction
Date Published:
Journal Name:
ACM Transactions on Human-Robot Interaction
Volume:
13
Issue:
4
ISSN:
2573-9522
Page Range / eLocation ID:
1 to 26
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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