Intention-Aware Motion Planning Using Learning Based Human Motion Prediction


Authors: Jae Sung Park, Chonhyon Park, Dinesh Manocha

We present a motion planning algorithm to compute collision-free and smooth trajectories for robots cooperating with humans in a shared workspaces. Our approach uses offline learning of human actions and their temporal coherence to predict the human actions at runtime. Our intention-aware online motion planning algorithm uses the learned database to compute a reliable trajectory based on the predicted actions. We represent the predicted human motion using a Gaussian distribution and compute tight upper bounds on collision probabilities for safe motion planning. We highlight the performance of our planning algorithm in complex simulated scenarios and real world benchmarks with 7-DOF robot arms operating in a workspace with a human performing complex tasks. We demonstrate the benefits of our intention-aware planner in terms of computing safe trajectories in such uncertain environments.