Relevance for Human Robot Collaboration

2024-09-12

arXiv.org

AI Summary

To address inefficiency, high latency, weak safety guarantees, and frequent human interventions in human-robot collaboration (HRC) caused by perceptual redundancy, this paper introduces a novel dimensionality reduction mechanism centered on *relevance*. Methodologically, we propose an event-driven continuous perception framework that integrates probabilistic graphical models with structured scene representations. We establish the first real-time relevance quantification paradigm tailored for HRC, design a cue sufficiency evaluation model, and develop a flexible perception-decision co-triggering mechanism. Experiments demonstrate: (i) relevance prediction F1-score of 0.96; (ii) 79.6% acceleration in task planning; (iii) 26.5% reduction in perception latency; (iv) 13.5% improvement in safety performance; and (v) 80.8% decrease in human-robot queries. These results validate the framework’s effectiveness in enabling seamless, context-aware assistance for everyday tasks.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Knowledge Representation and Reasoning: Common-Sense Reasoning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Develops relevance framework for human-robot collaboration efficiency
• Reduces perception latency and improves task planning time
• Enhances HRC safety and reduces human inquiries

Innovation

• Event-based framework for continuous scene perception
• Probabilistic methodology with structured scene representation
• Flexible relevance determination for HRC efficiency