发表机构
LIACS, Leiden University; VU Amsterdam（莱顿大学LIACS学院; 阿姆斯特丹自由大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过34名参与者的实验揭示人类教学决策的构成，提出TOSS框架，为理解教学决策、模拟神谕及设计机器人学习算法提供数据集与理论基础。
AI 中文摘要
成功的人机教学以机器人处理需求与人类教学意图的一致性为前提。为更好地理解这种一致性，本研究旨在揭示人类教学时直觉运用的底层逻辑。通过一项包含34名参与者的探索性自下而上研究，参与者观察两个不同的机器人强化学习（RL）场景，我们分析了早期、中期和晚期学习阶段的204个直觉教学响应。结果表明，教学决策由触发因素（情境催化剂）、目标（主观教学目标）、信号（交流行为）和策略（高层治理）构成的微妙互联网络组成，教师会自发承担教练、工程师或设计师等不同角色，并优先考虑不同目标。基于这些结果，我们提出TOSS框架，该框架将人机教学概念化为机器人行为与人类教学动作之间的程序循环，其中人类教学决策被建模为由教学目标和策略调节的触发-信号响应。它为未来研究提供了公开可访问的数据集和理论基础，用于：a）理解教学决策；b）模拟现实的“神谕”；c）设计以人为本的教学环境及新型机器人学习算法，突破当前机器人学习环境的限制。
英文摘要
Successful Human-Robot Teaching assumes alignment between robot processing needs and human teaching intent. To better understand this alignment, this work seeks to uncover the underlying logic that humans intuitively apply when teaching. Through an exploratory, bottom-up study with N=34, participants observing two distinct robot Reinforcement Learning (RL) scenarios, we analyze 204 intuitive teaching responses across early, middle, and late learning phases. Results reveal that teaching decisions consist of a nuanced, interconnected network of Triggers (situational catalysts), Objectives (subjective teaching targets), Signals (communicative acts), and Strategies (high-level governance) in which teachers spontaneously adopt diverse roles, acting as coaches, engineers, or designers and prioritize different objectives. Based on these results, we introduce the TOSS Framework, which conceptualizes Human-Robot teaching as a procedural loop between robot behavior and human teaching actions, in which human teaching decisions are modeled as Trigger-Signal responses modulated by teaching Objectives and Strategies. It provides future research with an openly accessible dataset and a theoretical foundation for a) understanding teaching decisions and b) simulating realistic oracles as well as c) designing human-centered teaching settings and novel robot learning algorithms that go beyond the constraints of current robot learning settings.
