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Asking the Right Questions: Facilitating Semantic Constraint Specification for Robot Skill Learning and Repair Öffentlichkeit Deposited

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https://scholar.colorado.edu/concern/articles/5425kc198
Abstract
  • Developments in human-robot teaming have given rise to significant interest in training methods that enable collaborative agents to safely and successfully execute tasks alongside human teammates. While effective, many existing methods are brittle to changes in the environment and do not account for the preferences of human collaborators. This ineffectiveness is typically due to the complexity of deployment environments and the unique personal preferences of human teammates. These complications lead to behavior that can cause task failure or user discomfort. In this work, we introduce Plan Augmentation and Repair through SEmantic Constraints (PARSEC): a novel algorithm that utilizes a semantic hierarchy to enable novice users to quickly and effectively select constraints using natural language that correct faulty behavior or adapt skills to their preferences. We show through a case study that our algorithm efficiently finds corrective constraints that match the user’s intent, providing a path for novice users to exploit the advantages of constrained motion planning combined with human-in-the-loop skill training.

Creator
Date Issued
  • 2021
Additional Information
  • © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Academic Affiliation
Journal Title
Zuletzt geändert
  • 2023-07-10
Resource Type
Urheberrechts-Erklärung
DOI
ISSN
  • 2153-0858
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