Semi-Supervised Learning of Decision-Making Models for Human-Robot Collaboration
Proceedings of the Conference on Robot Learning, PMLR 100:192-203, 2020.
We consider human-robot collaboration in sequential tasks with known task objectives. For interaction planning in this setting, the utility of models for decision-making under uncertainty has been demonstrated across domains. However, in practice, specifying the model parameters remains challenging, requiring significant effort from the robot developer. To alleviate this challenge, we present ADACORL, a framework to specify decision-making models and generate robot behavior for interaction. Central to our approach are a factored task model and a semi-supervised algorithm to learn models of human behavior. We demonstrate that our specification approach, despite significantly fewer labels, generates models (and policies) that perform equally well or better than models learned with supervised data. By leveraging pre-computed performance bounds and an online planner, ADACORL can generate robot behavior for collaborative tasks with large state spaces (> 1 million states) and short planning times (< 0.5 s).