Robots That Can Anticipate and Learn in Human-Robot Teams

Abstract

Robots are moving from working in isolated cham-bers to working in close-proximity with human collaborator(s) as part of human-robot teams. In such situations, robots are increasingly expected to work with multiple humans and ef-fectively model both human-human and human-robot dynamics before taking timely actions. Working toward this goal, we have proposed new algorithms that model human intent and motion while being interpretable and scalable to multiple humans. Our current work builds upon these algorithms to 1) obtain a more holistic representation of the environment and 2) interleave robot perception and control. Our proposed algorithms have attained state-of-the-art performances over various benchmarks and learning scenarios. As part of future work, we aim to enhance our learning algorithms with the capability of acquiring knowledge continually, without overwriting past information.