スクリプトベースとLLM強化の人間-ロボットインタラクションにおける効率性とエンゲージメントの評価
Evaluating Efficiency and Engagement in Scripted and LLM-Enhanced Human-Robot Interactions
産業用ロボットとの対話において、完全スクリプト制御とLLMによる応答強化を比較し、主観評価はLLMが高い一方、客観的な効率性や集中度はスクリプトと同等で、単純作業ではスクリプトが応答遅延や消費エネルギーで優位であることを示した。
著者: Tim Schreiter, Jens V. Rüppel, Rishi Hazra, Andrey Rudenko, Martin Magnusson, Achim J. Lilienthal
分類: cs.RO, cs.HC
原文アブストラクト
To achieve natural and intuitive interaction with people, HRI frameworks combine a wide array of methods for human perception, intention communication, human-aware navigation and collaborative action. In practice, when encountering unpredictable behavior of people or unexpected states of the environment, these frameworks may lack the ability to dynamically recognize such states, adapt and recover to resume the interaction. Large Language Models (LLMs), owing to their advanced reasoning capabilities and context retention, present a promising solution for enhancing robot adaptability. This potential, however, may not directly translate to improved interaction metrics. This paper considers a representative interaction with an industrial robot involving approach, instruction, and object manipulation, implemented in two conditions: (1) fully scripted and (2) including LLM-enhanced responses. We use gaze tracking and questionnaires to measure the participants' task efficiency, engagement, and robot perception. The results indicate higher subjective ratings for the LLM condition, but objective metrics show that the scripted condition performs comparably, particularly in efficiency and focus during simple tasks. We also note that the scripted condition may have an edge over LLM-enhanced responses in terms of response latency and energy consumption, especially for trivial and repetitive interactions.
