Relevance for Human Robot Collaboration

Abstract

Inspired by the human ability to selectively focus on relevant information, this paper introduces relevance, a novel dimensionality reduction process that enables robots to identify relevant scene elements in a scene and generate responses that are seamless, fast, and accurate. To accurately and efficiently quantify relevance, we developed an event-based framework that maintains a continuous perception of the scene, evaluates cue sufficiency within the scene, and selectively triggers relevance determination. Within this framework, we developed a probabilistic methodology that considers various factors and is built on a novel structured scene representation. Both simulations and experimental results demonstrate the effectiveness of our relevance concept, as well as the proposed framework and methods for relevance quantification. Simulation results demonstrate that the relevance framework and methodology accurately predict the relevance of a general Human Robot Collaboration (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 demonstrates broad benefits across multiple aspects of HRC, yielding a 79.56% reduction in task planning time compared with a state-of-the-art (SOTA) task planner for a cereal task, a 26.53% decrease in perception latency for object detection, an improvement of up to 13.50% in HRC safety, and an 80.84% reduction in the number of inquiries required during collaboration. A real-world demonstration highlights the effectiveness of the relevance framework, together with its modules, in providing intelligent and seamless assistance to humans during everyday tasks.