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2023 - 至今；开云平台首页 电信学部人工智能学院 教授
2010 - 2023，中科院百人计划研究员、博士生导师
中科院深圳先进院智能仿生中心副主任
2005 - 2010，美国里海大学 机械工程系 博士后研究
2004 - 2005，香港中文大学 自动化系 从事博士后研究
2000 - 2004，获香港中文大学 博士学位
1995 - 1998，获中科院自动化研究所 硕士学位
1991 - 1995，获北京航空航天大学 学士学位
入选的人才计划：
1) 国家“万人计划”创新领军人才
2) 中国科学院百人计划
3）科技部中青年领军人才推进计划
4) 广东省特支计划科技创新领军人才
5) 深圳市“孔雀计划”首批海外高层次人才
社会兼职：
1) 国家科技部十三五智能机器人重点专项总体组专家
2) 国家科技部十二五服务机器人重点专项组专家
3) 中国自动化专业委员会机器人专委会成员
4) 广东省机器人专家委员会副主任
5) IEEE Robotics and Automation Letters （JCR1区） 杂志的编委
6) IET Cyber-Systems & Robotics创刊编委
7) International Journal of Advanced Robotic Systems(SCI)编委（2011-今）
8) 担 任IEEE Transactions on Automation Science and Engineering 杂志(SCI)的专刊编委 (2014）
9) 担任IEEE Robotics and Automation Society Guangdong Chapter联合主席
10) IEEE senior member
Research Focus
- Robots play an important role in reducing labor costs and improving work efficiency and product quality. With the acceleration of product upgrading, the demand of small batch and multi-variety products for production line flexibility is becoming more and more obvious. However, there is still a big gap between most robots and human beings in terms of mechanism, perception and control, and it is difficult for robots to adapt to scenarios or task shifts. Humans, by contrast, are better at processing missing, contradictory and ambiguous information. The integration with human and environment is an important means to solve the problem of inadequate robot autonomy. This project focuses on robot assembly operation, carries out in-depth exploration of robot theory and practice in the case of Human-in-the-loop, develops methodologies of robot multi-modal perception, knowledge reasoning, precise and compliant control, and builds an intelligent robot verification system for complex assembly operations. The main research contents include: (1) Behavioral intention understanding method integrating environment perception and speech command; (2) Assembly process semantic inference and development mechanism based on knowledge graph; (3) Human-machine collaborative sharing control technology based on force contact. Finally, a robot assembly application demonstration for typical intelligent manufacturing scenarios is carried out to verify the proposed intelligent learning method.