NDLI: Learning Legible Motion from Human

Content Provider	Springer Nature Link
Author	Busch, Baptiste Grizou, Jonathan Lopes, Manuel Stulp, Freek
Copyright Year	2017
Abstract	In collaborative tasks, displaying legible behavior enables other members of the team to anticipate intentions and to thus coordinate their actions accordingly. Behavior is therefore considered to be legible when an observer is able to quickly and correctly infer the intention of the agent generating the behavior. In previous work, legible robot behavior has been generated by using model-based methods to optimize task-specific models of legibility. In our work, we rather use model-free reinforcement learning with a generic, task-independent cost function. In the context of experiments involving a joint task between (thirty) human subjects and a humanoid robot, we show that: (1) legible behavior arises when rewarding the efficiency of joint task completion during human–robot interactions (2) behavior that has been optimized for one subject is also more legible for other subjects (3) the universal legibility of behavior is influenced by the choice of the policy representation.
Starting Page	765
Ending Page	779
Page Count	15
File Format	PDF
ISSN	18754791
Journal	International Journal of Social Robotics
Volume Number	9
Issue Number	5
e-ISSN	18754805
Language	English
Publisher	Springer Netherlands
Publisher Date	2017-03-08
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Human–robot interaction (HRI) Legible motion Implicit coordination Reinforcement learning (RL) Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Philosophy Control and Systems Engineering Social Psychology Electrical and Electronic Engineering Human-Computer Interaction

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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