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  林天麟

  优德官网 智能机械人中心主任、香港中文大学（深圳）助理教授

  执行主席

  嘉宾先容

  演讲摘要
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  Amanda Prorok

  剑桥大学副教授

  Learning Cooperative Control Policies for Multi-Robot Systems

  嘉宾先容

  Amanda Prorok is an Associate Professor in the Department of Computer Science and Technology, at Cambridge University, and a Fellow of Pembroke College.

  Her lab's research focuses on multi-agent and multi-robot systems. Their mission is to find new ways of coordinating artificially intelligent agents (e.g., robots, vehicles, machines) to achieve common goals in shared physical and virtual spaces. This research brings in methods from machine learning, planning, and control, and has numerous applications, including automated transport and logistics, environmental monitoring, surveillance, and search.

  Prior to joining Cambridge, Amanda was a postdoctoral researcher at the General Robotics, Automation, Sensing and Perception (GRASP) Laboratory at the University of Pennsylvania, USA, where she worked with Prof. Vijay Kumar. She completed her PhD at EPFL, Switzerland, with Prof. Alcherio Martinoli. She has been honored by numerous research awards, including an ERC Starting Grant, an Amazon Research Award, the EPSRC New Investigator Award, the Isaac Newton Trust Early Career Award, and several Best Paper awards. Her PhD thesis was awarded the Asea Brown Boveri (ABB) prize for the best thesis at EPFL in Computer Science. She serves as Associate Editor for IEEE Robotics and Automation Letters (R-AL) and Associate Editor for Autonomous Robots (AURO). 

  演讲摘要

  In this talk, I discuss how we leverage machine learning methods to synthesize cooperative policies for multi-agent and multi-robot systems. I describe how we use Graph Neural Networks (GNNs) to model differentiable communications channels, and show how this enables decentralized coordination across various multi-agent tasks. I then describe the sim-to-real gap from a multi-robot perspective, and conclude by discussing the role of agent heterogeneity and its impact on transferring learned cooperative policies to real-world experimental settings.

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  Malika Meghjani

  新加坡科技设计大学助理教授

  Real-World Applications of Heterogeneous Multi-Robot Sytems

  嘉宾先容

  Dr. Malika Meghjani is an Assistant Professor in the Computer Science and Design Pillar at Singapore University of Technology and Design (SUTD). She directs the Multi-Agent Robot Vision and Learning (MARVL) Lab, with the focus on algorithm design for efficient, reliable and scalable robots that can work independently and collaboratively with humans. Her research interests are in planning under uncertainty, reinforcement learning, computer vision, deep learning, and game theory. The applications of her work are in field robotics ranging from marine robots specifically, underwater and surface vehicles to aerial drones and selfdriving cars as well as other ground vehicles in unstructured environments. Malika has been cited by Analytics Insight in 2020 as one of the World's 50 Most Renowned Women in Robotics. She is also 2017 SMART Postdoctoral Scholar, 2015 McGill Scarlet Key recipient, 2013 IEEE Canada Women in Engineering Prize awardee and 2013 Google Anita Borg Scholar.

  演讲摘要

  Real-world applications with multiple objectives can greatly benefit when multi-robot systems are heterogeneous. In this talk, I will present some of the applications for which we use heterogeneous multi-robot systems. These include dynamic environment monitoring, searching for moving and evading targets, mobility-on-demand, and urban reconnaissance. The heterogeneity in the proposed applications is showcased in terms of the robots in different domains as well as robots with different capabilities in the same domain.

视频回首

（Amanda Prorok教授的讲座仅直播无回放）