Transferring human navigation behaviors into a robot local planner

Robot navigation in human environments is an active research area that poses serious challenges. Among them, social navigation and human-awareness has gain lot of attention in the last years due to its important role in human safety and robot acceptance. Learning has been proposed as a more principl...

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Bibliographic Details
Main Authors: Ramon-Vigo, Rafael, Perez-Higueras, Noe, Caballero, Fernando, Merino, Luis
Format: Conference Proceeding
Language:English
Subjects:
Angular velocity
Cost function
Navigation
Planning
Robot kinematics
Training
Online Access:Request full text
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Summary:Robot navigation in human environments is an active research area that poses serious challenges. Among them, social navigation and human-awareness has gain lot of attention in the last years due to its important role in human safety and robot acceptance. Learning has been proposed as a more principled way of estimating the insights of human social interactions. In this paper, inverse reinforcement learning is analyzed as a tool to transfer the typical human navigation behavior to the robot local navigation planner. Observations of real human motion interactions found in one publicly available datasets are employed to learn a cost function, which is then used to determine a navigation controller. The paper presents an analysis of the performance of the controller behavior in two different scenarios interacting with persons, and a comparison of this approach with a Proxemics-based method.
ISSN:1944-9445
1944-9437
DOI:10.1109/ROMAN.2014.6926347