Planning-based prediction for pedestrians

We present a novel approach for determining robot movements that efficiently accomplish the robot's tasks while not hindering the movements of people within the environment. Our approach models the goal-directed trajectories of pedestrians using maximum entropy inverse optimal control. The adva...

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Bibliographic Details
Published in2009 IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 3931 - 3936
Main Authors Ziebart, B.D., Ratliff, N., Gallagher, G., Mertz, C., Peterson, K., Bagnell, J.A., Hebert, M., Dey, A.K., Srinivasa, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2009
Subjects
Computer science
Cost function
Entropy
Hidden Markov models
Intelligent robots
Optimal control
Predictive models
Trajectory
Uncertainty
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Summary:We present a novel approach for determining robot movements that efficiently accomplish the robot's tasks while not hindering the movements of people within the environment. Our approach models the goal-directed trajectories of pedestrians using maximum entropy inverse optimal control. The advantage of this modeling approach is the generality of its learned cost function to changes in the environment and to entirely different environments. We employ the predictions of this model of pedestrian trajectories in a novel incremental planner and quantitatively show the improvement in hindrance-sensitive robot trajectory planning provided by our approach.
ISBN:9781424438037
1424438039
ISSN:2153-0858
DOI:10.1109/IROS.2009.5354147