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 hindrancesensitive robot trajectory planning provided by our approach. Determining appropriate robotic actions in environments with moving people is a well-studied [15], [2], [5], but often difficult task due to the uncertainty of each person's future behavior. Robots should certainly never collide with people [11], but avoiding collisions alone is often unsatisfactory because the disruption of almost colliding can be burdensome to people and sub-optimal for robots. Instead, robots should predict the future locations of people and plan routes that will avoid such hindrances (i.e., situations where the person's natural behavior is disrupted due to a robot's proximity) while still efficiently achieving the robot's objectives. For example, given the origins and target destinations of the robot and person in Figure 1 , the robot's hindrance-minimizing trajectory would take the longer way around the center obstacle (a table), leaving a clear path for the pedestrian. One common approach for predicting trajectories is to project the prediction step of a tracking filter [9] , [13] , [10] forward over time. For example, a Kalman filter's [7] future positions are predicted according to a Gaussian distribution with growing uncertainty and, unfortunately, often high probability for physically impossible locations (e.g., behind walls, within obstacles). Particle filters [16] can incorporate more sophisticated constraints and non-Gaussian distributions, but degrade into random walks of feasible motion over large time horizons rather than purposeful, goal-based motion. Closer to our research are approaches that directly model the policy [6] . These approaches assume that previously observed trajectories capture all purposeful behavior, and the only uncertainty involves determining to which previously observed class of trajectories the current behavior belongs. Models based on mixtures of trajectories and conditioned action distribution modeling (using hidden Markov models) have been employed [17] . This approach often suffers from over-fitting to the particular training trajectories and context of those trajectories. When changes to the environment occur (e.g., rearrangement of the furniture), the model will confidently predict incorrect trajectories through obstacles.
REF learns the cost function of the environment via observed trajectories.
Planning-based prediction for pedestrians
2009 IEEE/RSJ International Conference on Intelligent Robots and Systems
2009 IEEE/RSJ International Conference on Intelligent Robots and Systems
Computer Science