Interactive Robot Training for Non-Markov Tasks

• URL: http://arxiv.org/abs/2003.02232v2
• Date: Sat, 28 Nov 2020 17:03:57 GMT
• Title: Interactive Robot Training for Non-Markov Tasks
• Authors: Ankit Shah, Samir Wadhwania, Julie Shah
• Abstract summary: We propose a Bayesian interactive robot training framework that allows the robot to learn from both demonstrations provided by a teacher. We also present an active learning approach to identify the task execution with the most uncertain degree of acceptability. We demonstrate the efficacy of our approach in a real-world setting through a user-study based on teaching a robot to set a dinner table.
• Score: 6.252236971703546
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Defining sound and complete specifications for robots using formal languages is challenging, while learning formal specifications directly from demonstrations can lead to over-constrained task policies. In this paper, we propose a Bayesian interactive robot training framework that allows the robot to learn from both demonstrations provided by a teacher, and that teacher's assessments of the robot's task executions. We also present an active learning approach -- inspired by uncertainty sampling -- to identify the task execution with the most uncertain degree of acceptability. Through a simulated experiment, we demonstrate that our active learning approach identifies a teacher's intended task specification with an equivalent or greater similarity when compared to an approach that learns purely from demonstrations. Finally, we demonstrate the efficacy of our approach in a real-world setting through a user-study based on teaching a robot to set a dinner table.

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