Anticipatory robot path planning in human environments

Robot path planning in human environments benefits significantly from considering more than obstacle avoidance, and recent works in this area proposed safety and comfort considerations. One shortcoming of current approaches is that humans' behavior is modeled as independent of robot's motions. In this work, we aim to give this anticipation ability to a robot by simulating people's reaction to robot's motion during planning. Our approach is based on extracting a static plan using A* search on the grid map by minimizing safety, disturbance and path length costs and then refining it by simulating humans' reaction using the Social Force Model. With two example scenarios in simulation and two on the real system, we provide qualitative examination of the resulting robot paths and demonstrate that robots can exhibit social behaviors that is not possible to model with standard approaches. This work serves as a primer for quantitative user studies, and we hope will urge future robot path planners to consider a richer set of social capabilities.