本研究主要基于人的运动神经机理，如人的运动控制学习，运动协调和人-人协作机制，以实现机器人的类人化认知操作及交互控制。拟基于多种人体穿戴式传感器及视觉，力信息的融合，实现人的冗余运动学和动力学建模，在此基础上，构建类人化机械人手臂冗余运动学和自适应学习变阻抗控制器模型，并通过开展人机协作及多人-多机协作等安全柔顺性的人机交互控制试验应用研究，验证机器人类人化认知操作及交互控制策略的安全性，稳定性，柔顺性和普适性。该研究将为机器人无缝融入人的工作空间，与人协作提供相应的理论支持和技术积累，能够显著提高机器人的智能性、灵巧性、多功能性和适应性，相关研究对下一代的机器人系统开发具有重要意义。

This research topic is proposed to exploit anthropomorphic and cognitive manipulation and its interactive application on robot arm, based on human motor control theories. This includes adaptive motor learning and coordination as well as human-human cooperation. Human kinematics and dynamics modelling with redundancy are established by employing multi-sensor fusion from vision, force, motion etc. Anthropomorphic kinematics and dynamics modelling of robot arm will be investigated based on human motor control research. Anthropomorphic and cognitive manipulation performance such as safety, stability, flexibility and usability will be verified by compliant interaction under the scenarios: human- robot collaboration and multi- human- robot cooperation in routine tasks. This research will enable robot to share the same workspace with our human beings, and supply primitives and technologies of human robot collaboration. It may improve robot intelligence, dexterity, versatility and adaption. Related research has important implications for the development of the next generation of robotic systems.