Interactive Learning via Physical Human Feedback Using Uncertainty-Aware Energy Tanks

Abstract

Learning from demonstration (LfD) offers an intuitive alternative to manual coding by leveraging natural human behavior, while Human-Robot Interaction (HRI) provides an intuitive means to refine and adapt learned skills. Among interaction modalities, physical contact is a natural and effective way to convey intent. In order to leverage such modality, robots need to be able to distinguish physical contacts coming from deliberate human interactions (e.g. to correct a learned skill) from those from environmental factors (e.g. task-related). In this paper, we introduce a novel interactive framework for physically modulating learned robot skills. Our method builds on a state-of-the-art energy-tank-based intention detection approach to capture degree-of-freedom(DoF)-specific modulations and, accordingly, incorporate user-defined via-points into the learned skills. In contrast to common approaches, corrections are applied selectively to the relevant DoFs, preserving the original skill behavior in the remaining dimensions. Moreover, we leverage uncertainty in the demonstration data to modulate the tank dynamics, allowing users more or less time to intervene in regions of different uncertainty. We validate our approach on a torque-controlled 7-DoF robot executing a learned task of inserting a bearing ring, where physical human corrections are used to adapt to changes in the environment.