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Learning Motion Patterns of People for Compliant Robot Motion

Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany

Wolfram Burgard

Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany

Grzegorz Cielniak

Department of Technology, Örebro University, 70182 Örebro, Sweden

Sebastian Thrun

Computer Science Department, Stanford University, Stanford, CA, USA

Whenever people move through their environments they do not move randomly. Instead, they usually follow specific trajectories or motion patterns corresponding to their intentions. Knowledge about such patterns enables a mobile robot to robustly keep track of persons in its environment and to improve its behavior. In this paper we propose a technique for learning collections of trajectories that characterize typical motion patterns of persons. Data recorded with laser-range finders are clustered using the expectation maximization algorithm. Based on the result of the clustering process, we derive a hidden Markov model that is applied to estimate the current and future positions of persons based on sensory input. We also describe how to incorporate the probabilistic belief about the potential trajectories of persons into the path planning process of a mobile robot. We present several experiments carried out in different environments with a mobile robot equipped with a laser-range scanner and a camera system. The results demonstrate that our approach can reliably learn motion patterns of persons, can robustly estimate and predict positions of persons, and can be used to improve the navigation behavior of a mobile robot.

Key Words: learning activity models • trajectory clustering • machine learning • mobile robot navigation • human robot interaction

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				D. Kulic, W. Takano, and Y. Nakamura Incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains The International Journal of Robotics Research, July 1, 2008; 27(7): 761 - 784. [Abstract] [PDF]