C. Quintero-Peña, C. Chamzas, V. Unhelkar, and L. E. Kavraki, “Motion Planning via Bayesian Learning in the Dark,” in ICRA 2021: Workshop on Machine Learning for Motion Planning, 2021.
Motion planning is a core problem in many applications spanning from robotic manipulation to autonomous driving. Given its importance, several schools of methods have been proposed to address the motion planning problem. However, most existing solutions require complete knowledge of the robot’s environment; an assumption that might not be valid in many real-world applications due to occlusions and inherent limitations of robots’ sensors. Indeed, relatively little emphasis has been placed on developing safe motion planning algorithms that work in partially unknown environments. In this work, we investigate how a human who can observe the robot’s workspace can enable motion planning for a robot with incomplete knowledge of its workspace. We propose a framework that combines machine learning and motion planning to address the challenges of planning motions for high-dimensional robots that learn from human interaction. Our preliminary results indicate that the proposed framework can successfully guide a robot in a partially unknown environment quickly discovering feasible paths.