Getting to Know One Another: Calibrating Intent, Capabilities and Trust for Human-Robot Collaboration
This addresses the challenge of improving collaboration between humans and robots in scenarios with limited communication and unknown capabilities, though it appears incremental as it builds on existing decision-theoretic frameworks.
The paper tackles the problem of calibrating intention and capabilities in human-robot collaboration where the human cannot directly communicate intent and capabilities are unknown, proposing a TICC-POMDP model with an online solver that leads to better team performance in simulation and real-world studies.
Common experience suggests that agents who know each other well are better able to work together. In this work, we address the problem of calibrating intention and capabilities in human-robot collaboration. In particular, we focus on scenarios where the robot is attempting to assist a human who is unable to directly communicate her intent. Moreover, both agents may have differing capabilities that are unknown to one another. We adopt a decision-theoretic approach and propose the TICC-POMDP for modeling this setting, with an associated online solver. Experiments show our approach leads to better team performance both in simulation and in a real-world study with human subjects.