Relevance for Human Robot Collaboration

• URL: http://arxiv.org/abs/2409.07753v2
• Date: Sat, 12 Oct 2024 20:19:12 GMT
• Title: Relevance for Human Robot Collaboration
• Authors: Xiaotong Zhang, Dingcheng Huang, Kamal Youcef-Toumi,
• Abstract summary: This paper introduces a novel concept and scene-understanding approach termed relevance' To accurately and efficiently quantify relevance, we developed an event-based framework that selectively triggers relevance determination. A real-world demonstration showcases the relevance framework's ability to intelligently assist humans in everyday tasks.
• Score: 6.009969292588733
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective human-robot collaboration (HRC) requires the robots to possess human-like intelligence. Inspired by the human's cognitive ability to selectively process and filter elements in complex environments, this paper introduces a novel concept and scene-understanding approach termed `relevance.' It identifies relevant components in a scene. To accurately and efficiently quantify relevance, we developed an event-based framework that selectively triggers relevance determination, along with a probabilistic methodology built on a structured scene representation. Simulation results demonstrate that the relevance framework and methodology accurately predict the relevance of a general HRC setup, achieving a precision of 0.99 and a recall of 0.94. Relevance can be broadly applied to several areas in HRC to improve task planning time by 79.56% compared with pure planning for a cereal task, reduce perception latency by up to 26.53% for an object detector, improve HRC safety by up to 13.50% and reduce the number of inquiries for HRC by 80.84%. A real-world demonstration showcases the relevance framework's ability to intelligently assist humans in everyday tasks.

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