Learning equivalent action choices from demonstration

Abstract— In their interactions with the world robots inevitably face equivalent action choices, situations in which multiple actions are equivalently applicable. In this paper, we address the problem of equivalent action choices in learning from demonstration, a robot learning approach in which a policy is acquired from human demonstrations of the desired behavior. We note that when faced with a choice of equivalent actions, a human teacher often demonstrates an action arbitrarily and does not make the choice consistently over time. The resulting inconsistently labeled training data poses a problem for classification-based demonstration learning algorithms by violating the common assumption that for any world state there exists a single best action. This problem has been overlooked by previous approaches for demonstration learning. In this paper, we present an algorithm that identifies regions of the state space with conflicting demonstrations and enables the choice between multi...

• Type: Conference
• Year: 2008
• Where: IROS
• Authors: Sonia Chernova, Manuela M. Veloso
• Keywords: Demonstration Learning, Equivalent Action Choices, Equivalent Actions, IROS 2008, Robotics