Year: 2020
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Shah, Ankit, Wadhwania, Samir, Shah, Julie
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W3009614262