Influencing Behavioral Attributions to Robot Motion During Task Execution
Proceedings of the 5th Conference on Robot Learning, PMLR 164:169-179, 2022.
While prior work has shown how to autonomously generate motion that communicates task-related attributes, like intent or capability, we know less about how to automatically generate motion that communicates higher-level behavioral attributes such as curiosity or competence. We propose a framework that addresses the challenges of modeling human attributions to robot motion, generating trajectories that elicit attributions, and selecting trajectories that balance attribution and task completion. The insight underpinning our approach is that attributions can be ascribed to features of the motion that don’t severely impact task performance, and that these features form a convenient basis both for predicting and generating communicative motion. We illustrate the framework in a coverage task resembling household vacuum cleaning. Through a virtual interface, we collect a dataset of human attributions to robot trajectories during task execution and learn a probabilistic model that maps trajectories to attributions. We then incorporate this model into a trajectory generation mechanism that balances between task completion and communication of a desired behavioral attribute. Through an online user study on a different household layout, we find that our prediction model accurately captures human attribution for coverage tasks.