Inverse Learning of Robot Behavior for Collaborative Planning

Abstract

Inverse reinforcement learning (IRL) is an important basis for learning from demonstrations. Observing an agent, human or robotic, perform a task provides information and facilitates learning the task. We show how the agent's preferences learned using IRL can be incorporated in a subject robot's decision making and planning, to enable the robot to spontaneously collaborate with the previously observed agent on the task. We prioritize a real-world application, where a line robot will autonomously collaborate with another robot in sorting ripe and unripe fruit such as oranges. Toward this, our evaluations utilize a colored-ball sorting task as an analog using simulated TurtleBots equipped with Phantom X arms. Our method is comprehensive providing first answers to questions such as how should the robot acquire the complete model for the collaborative planning problem and how should it solve the problem to obtain a plan that permits collaboration without disrupting the line robot's behavior.