Asymptotically Optimal Motion Planning for Learned Tasks Using Time-Dependent Cost Maps.

In unstructured environments in people's homes and workspaces, robots executing a task may need to avoid obstacles while satisfying task motion constraints, e.g., keeping a plate of food level to avoid spills or properly orienting a finger to push a button. We introduce a sampling-based method for computing motion plans that are collision-free and minimize a cost metric that encodes task motion constraints. Our time-dependent cost metric, learned from a set of demonstrations, encodes features of a task's motion that are consistent across the demonstrations and, hence, are likely required to successfully execute the task. Our sampling-based motion planner uses the learned cost metric to compute plans that simultaneously avoid obstacles and satisfy task constraints. The motion planner is asymptotically optimal and minimizes the Mahalanobis distance between the planned trajectory and the distribution of demonstrations in a feature space parameterized by the locations of task-relevant objects. The motion planner also leverages the distribution of the demonstrations to significantly reduce plan computation time. We demonstrate the method's effectiveness and speed using a small humanoid robot performing tasks requiring both obstacle avoidance and satisfaction of learned task constraints.

NOTE TO PRACTITIONERS: Motivated by the desire to enable robots to autonomously operate in cluttered home and workplace environments, this paper presents an approach for intuitively training a robot in a manner that enables it to repeat the task in novel scenarios and in the presence of unforeseen obstacles in the environment. Based on user-provided demonstrations of the task, our method learns features of the task that are consistent across the demonstrations and that we expect should be repeated by the robot when performing the task. We next present an efficient algorithm for planning robot motions to perform the task based on the learned features while avoiding obstacles. We demonstrate the effectiveness of our motion planner for scenarios requiring transferring a powder and pushing a button in environments with obstacles, and we plan to extend our results to more complex tasks in the future.