Abstract:Â
Collaborative robots have the ability to adapt and improve their behavior by learning from their human users. By interactively learning on the job, these robots can both acquire new motor skills and customize their behavior to personal user preferences. However, for this paradigm to be viable, there must be a balance between teaching the robot necessary skills, minimizing user burden, and maintaining task progress. We propose COIL, a novel polynomial-time interaction planner that explicitly minimizes human effort while ensuring the completion of a given sequence of tasks according to hidden user preferences. When user preferences are known, we formulate this planning-to-learn problem as an uncapacitated facility location problem. COIL utilizes efficient approximation algorithms for facility location to plan in the case of unknown preferences in polynomial time. In contrast, prior methods do not guarantee minimization of human effort nor consider the inherently collaborative nature of learning on the job, in which timely task execution may require the robot to forego learning and instead request human contributions. Simulated and physical experiments on manipulation tasks show that our framework significantly reduces the amount of work allocated to the human while maintaining successful task completion.