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.

翻译：协作机器人（cobots）正日益广泛地集成于各类工业与服务场景中，以高效、安全地与人类协同工作。然而，为实现有效的人机协作，机器人必须基于人类因素（如动机水平与攻击性水平）进行推理。本文提出一种利用随机建模的人机协作环境决策方法。通过运用概率模型与控制策略，所提方法旨在预测人类行为与情绪，从而使协作机器人能够相应地调整自身行为。迄今为止，多数研究集中于检测人类协作伙伴的意图。本文讨论了双边协作方法的理论框架、实施策略、仿真结果及其在协作机器人安全与效率方面的潜在应用。