Relevance for Human Robot Collaboration

TL;DR

This paper presents a novel relevance-based dimensionality reduction framework for human-robot collaboration, improving efficiency, safety, and perception accuracy through a probabilistic, event-driven perception system.

Contribution

It introduces a new relevance evaluation method with a continuous perception module and a probabilistic scene representation, enhancing HRC task performance.

Findings

Achieves 0.99 precision and 0.94 recall in relevance prediction

Reduces task planning time by 79.56% in cereal tasks

Decreases perception latency by up to 26.53%

Abstract

Inspired by the human ability to selectively focus on relevant information, this paper introduces relevance, a novel dimensionality reduction process for human-robot collaboration (HRC). Our approach incorporates a continuously operating perception module, evaluates cue sufficiency within the scene, and applies a flexible formulation and computation framework. To accurately and efficiently quantify relevance, we developed an event-based framework that maintains a continuous perception of the scene and selectively triggers relevance determination. Within this framework, we developed a probabilistic methodology, which considers various factors and is built on a novel 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, a recall of 0.94, an F1 score of 0.96, and an object ratio of 0.94. Relevance can be broadly applied to several areas in HRC to accurately 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 and seamlessly assist humans in everyday tasks.