Robots operating in human environments must not only ensure physical safety but also exhibit behaviors that are understandable, fluent, and acceptable to human partners. This paper investigates motion generation strategies that combine safety guarantees with interaction quality considerations, such as motion smoothness and human comfort. While the design of robots capable of ensuring safety in shared human-robot environments has enabled closer and more advanced forms of interaction, these new proximity-based tasks require moving beyond purely technical considerations. In particular, robot behavior must also be addressed from psycho-cognitive and social perspectives. In this context, we argue for the relevance of integrating social-aware motion control into robotic systems. First, we identify the motion parameters that influence human perception and operator experience. Then, we implement a Model Predictive Control (MPC) framework that generates four distinct socially-informed robot behaviors. Finally, we conduct a user study to evaluate and validate these behaviors and assess their social impact on non-expert participants. The results demonstrate that variations in robot behavior significantly affect the perceived social acceptability of the system. These findings highlight the importance of incorporating human-centered considerations into motion generation strategies for robots operating in shared environments.

翻译：在人类环境中运行的机器人不仅要确保物理安全，还需表现出人类伙伴可理解、流畅且可接受的行为。本文研究了结合安全保障与交互质量考量（如运动平滑性和人类舒适度）的运动生成策略。尽管能够确保共享人机环境中安全性的机器人设计已促成更紧密、更高级的交互形式，但这些基于近距离的新任务要求超越纯技术层面的考量。具体而言，机器人行为还需从心理认知与社会角度进行探讨。在此背景下，我们论证了将社交感知运动控制集成到机器人系统中的相关性。首先，我们识别了影响人类感知与操作员体验的运动参数；其次，我们实现了一个能够生成四种不同社交化机器人行为的模型预测控制框架；最后，我们开展了一项用户研究，以评估和验证这些行为及其对非专业参与者的社会影响。结果表明，机器人行为的显著变化会影响系统的社会接受度感知。这些发现凸显了将人类中心考量纳入共享环境机器人运动生成策略的重要性。