用随机模型让协作机器人感知人的情绪与动机，实时调整行为。
Stochastic Decision-Making Framework for Human-Robot Collaboration in Industrial Applications
- 基于概率模型预测人类情绪与意图，动态调整机器人动作
- 通过仿真验证框架可提升人机协作安全与效率
- 适合需要高安全性的工业人机协同场景
协作机器人（cobots）正越来越多地应用于工业和服务场景中，与人类协同工作。然而，要实现高效且安全的人机协作，机器人必须基于人类因素（如动机水平和攻击性水平）进行推理。本文提出一种在人机协作（HRC）环境中使用随机建模进行决策的方法。通过利用概率模型和控制策略，该方法旨在预测人类的行为与情绪，使协作机器人能够相应调整自身行为。目前多数研究聚焦于检测人类同事的意图，而本文则探讨了双边协作框架的理论基础、实施策略、仿真结果及潜在应用，以实现协作机器人在安全性与效率方面的优化。
原文摘要 · Abstract (English)
Collaborative robots, or cobots, are increasingly integrated into various industrial and service settings to work efficiently and safely alongside humans. However, for effective human-robot collaboration, robots must reason based on human factors such as motivation level and aggression level. This paper proposes an approach for decision-making in human-robot collaborative (HRC) environments utilizing stochastic modeling. By leveraging probabilistic models and control strategies, the proposed method aims to anticipate human actions and emotions, enabling cobots to adapt their behavior accordingly. So far, most of the research has been done to detect the intentions of human co-workers. This paper discusses the theoretical framework, implementation strategies, simulation results, and potential applications of the bilateral collaboration approach for safety and efficiency in collaborative robotics.
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