Interactive Robot Learning from Non-Expert Human Teachers

Abstract:
Today’s general-purpose robot learning policies are limited to 50-80% zero-shot performance on downstream tasks. To close this performance gap, deployed robots face the ongoing challenge of continually learning and adapting to diverse downstream tasks on demand under human guidance. Existing frameworks allow experts to guide robots in various ways including providing reward functions, demonstrations, and corrective feedback. However, most robots will only have access to non-expert users for guidance after deployment. At the same time, traditional machine learning methods often used in robot learning are tethered to the expectation of informative and near-optimal data. Novice human teachers—rich in practical experience yet lacking in robotics and engineering knowledge—bring data that strays from this ideal. My research addresses the challenges of robot learning brought by non-expert teachers and enables robots to effectively learn under non-expert human guidance, including developing active reward learning algorithms to allow the robot to take an active role in learning by asking informative questions, leveraging a hybrid action representation for imitation learning that is more robust to suboptimal demonstrations, and enabling the robot to interpret the human teacher’s natural feedback in the form of facial expressions, language, and gestures. Tapping into diverse sources of non-expert human feedback can lead to successful robot policies that can effectively work alongside humans and learn from them.

Bio:
Yuchen Cui is currently an Assistant Professor of Computer Science at UCLA, directing the UCLA Robot Intelligence Lab. Prior to UCLA, she was a postdoc in the Computer Science Department at Stanford University and a fellow of the Stanford Institute for Human-Centered AI. Yuchen’s research focuses on interactive robot learning and specifically on how to enable low-effort teaching for non-expert users. Yuchen obtained her Ph.D. in Computer Science from the University of Texas at Austin. Her dissertation is titled “Efficient algorithms for low-effort human teaching of robots”. During her graduate studies, Yuchen also conducted internships at Honda Research Institute, Diligent Robotics, and Facebook AI Research.