Learning How Pedestrians Navigate: A Deep Inverse Reinforcement Learning Approach

Muhammad Fahad, Zhuo Chen, Yi Guo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

39 Scopus citations

Abstract

Humans and mobile robots will be increasingly cohabiting in the same environments, which has lead to an increase in studies on human robot interaction (HRI). One important topic in these studies is the development of robot navigation algorithms that are socially compliant to humans navigating in the same space. In this paper, we present a method to learn human navigation behaviors using maximum entropy deep inverse reinforcement learning (MEDIRL). We use a large open dataset of pedestrian trajectories collected in an uncontrolled environment as the expert demonstrations. Human navigation behaviors are captured by a nonlinear reward function through deep neural network (DNN) approximation. The developed MEDIRL algorithm takes feature inputs including social affinity map (SAM) that are extracted from human motion trajectories. We perform simulation experiments using the learned reward function, and the performance is evaluated comparing it with the real measured pedestrian trajectories in the dataset. The evaluation results show that the proposed method has acceptable prediction accuracy compared to other state-of-the-art methods, and it can generate pedestrian trajectories similar to real human trajectories with natural social navigation behaviors such as collision avoidance, leader-follower, and split-and-rejoin.

Original languageEnglish
Title of host publication2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
Pages819-826
Number of pages8
ISBN (Electronic)9781538680940
DOIs
StatePublished - 27 Dec 2018
Event2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018 - Madrid, Spain
Duration: 1 Oct 20185 Oct 2018

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
Country/TerritorySpain
CityMadrid
Period1/10/185/10/18

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