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评论:【百家大讲堂】第128期:Towards agile robots in unstructured environments: decision, planning, and control

发布日期:2018-11-02
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海淀文龙学院s new roman,times,serif">Towards agile robots in unstructured environments: decision, planning, and control

  报 告 人:Zhao Ye 赵烨 教授

  时  间:2018年11月12日(周一)14:00

  地  点中关村校区研究生教学楼5楼创新基地(电梯出口北侧)

  主办单位:研究生院、自动化学院

  报名方式:登录北京理工大学微信企业号---第二课堂---课程报名中选择“百家大讲堂第128期:Towards agile robots in unstructured environments: decision, planning, and control

 

【主讲人简介】

 

  赵烨是哈佛大学John A. Paulson工程与应用科学学院的博士后研究员,并将于2019年1月在佐治亚理工学院的George W. Woodruff机械工程学院担任助理教授,任佐治亚理工学院机器人和智能机器研究所成员。他于2016年获得德克萨斯大学奥斯汀分校的机械工程博士学位和UT机器人联合学位。他于2011年获得哈尔滨工业大学控制科学与工程学士学位。他的研究兴趣主要集中在高动态机器人的规划、控制和优化算法设计。

 

Ye Zhao is a postdoctoral fellow in the School of Engineering and Applied Science at Harvard University. He will start as an Assistant Professor in The George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology in January 2019. He will be a member of Institute for Robotics and Intelligent Machines at Georgia Tech. Dr. Zhao received his Ph.D. degree in Mechanical Engineering from The University of Texas at Austin in 2016, where he also obtained the UT Robotics Portfolio degree and was a member of the Human Centered Robotics Laboratory. He received his bachelor degree from Control Science and Engineering at Harbin Institute of Technology in 2011. His research interests lie broadly in planning, control, and optimization algorithms of highly dynamic, contact-rich, and human-centered robots. He is especially interested in challenging locomotion and manipulation problems with formal guarantees on robustness, agility, real-time, and autonomy. Ye's co-authored work has been recognized as the 2017 Thomson Reuters Highly Cited Paper and 2016 IEEE-RAS best whole-body control paper award finalist. He serves as an ICT Chair of IROS 2018 and was a Co-Chair of the IEEE-RAS Student Activities Committee in 2016.

 

【讲座摘要】

  本报告将讨论实现敏捷和以人为中心的机器人所必需的新计算方法,特别关注在粗糙地形上高度敏捷的腿部运动的稳健最优运动规划。该规划方法围绕鲁棒混合自动机,由相空间流形定义的干扰度量,动态编程恢复控制器以及在实际系统中执行的在线运动规划。将这种运动规划方法扩展到广义多接触运动行为,描述高层反应任务规划器综合,用于与受约束环境相互作用的全身运动,以及如何整合形式化的任务能力运动方法。报告还将介绍偏置最大似然轨迹优化算法的最新进展,无需考虑接触模式并能够处理接触和模型不确定性。这次报告将以赵烨教授在佐治亚理工学院实验室的未来发展方向结束。

 

This talk will discuss new computational approaches necessary to enable agile and human-centered robots with a special focus on robust optimal motion planning of highly agile legged locomotion over rough terrain. This planning approach revolves around robust hybrid automaton, disturbance metric defined by phase-space manifolds, dynamic programming recovery controller, and online foot placement re-planning for execution in real systems. Extending this motion planning approach to generalized multi-contact locomotion behaviors, I will describe high-level reactive task planner synthesis for whole-body locomotion interacting with constrained environments and how to integrate formal methods for mission-capable locomotion. This talk will also present recent progress on biased maximum likelihood trajectory optimization algorithm without enumerating contact modes and capable of handling contact and model uncertainties. Then it will outline my research on distributed whole-body operational space control and impedance control for latency-prone robotic systems. This talk will end with future directions in my lab at Georgia Tech. If you are interested in joining my lab as a graduate student or visiting scholar, please feel free to talk with me after the talk.


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