Recent PhD graduate Jake Brawer won the best technical paper award at the 18th
The full paper is available here.
One important aspect of effective human–robot collaborations is the ability for robots to adapt quickly to the needs of humans. While techniques like deep reinforcement learning have demonstrated success as sophisticated tools for learning robot policies, the fluency of human-robot collaborations is often limited by these policies’ inability to integrate changes to a user’s preferences for the task. To address these shortcomings, we propose a novel approach that can modify learned policies at execution time via symbolic if-this-thenthat rules corresponding to a modular and superimposable set of low-level constraints on the robot’s policy. These rules, which we call Transparent Matrix Overlays, function not only as succinct and explainable descriptions of the robot’s current strategy but also as an interface by which a human collaborator can easily alter a robot’s policy via verbal commands. We demonstrate the efficacy of this approach on a series of proof-of-concept cooking tasks performed in simulation and on a physical robot.