Planning-based Prediction for Pedestrians

Brian D. Ziebart, Nathan Ratliff, Garratt Gallaghey, Christoph Mertz, Kevin Peterson, J. Andrew Bagnell, Martial Hebert, Anind K. Dey, and Siddhartha Srinivasa.
IEEE Conference on Intelligent Robots and Systems (IROS 2009).
[pdf]

Abstract: 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.

Bibtex:

@inproceedings{ratliff-iohc,
   author = {Brian D. Ziebart and Nathan Ratliff and Garratt Gallagher and
             Christoph Mertz and Kevin Peterson and J. Andrew Bagnell and 
             Martial Hebert and Anind K. Dey and Siddhartha Srinivasa},
   title = {Inverse Optimal Heuristic Control for Imitation Learning},
   year = {2009},
   booktitle = {Proc. IROS}
}