AIRS in the AIR
AIRS in the AIR | 卡耐基梅隆大学机器人研究所暑期学者分享
卡耐基梅隆大学(CMU)机器人研究所本科生暑期研究项目(RISS)历时11周,集结多位研究最前沿机器人技术的国际知名专家,是全球最顶级的本科生机器人暑期研究项目。
本期 AIRS in the AIR 邀请2022年度参加 RISS 项目的本科生同学肖文力和林沐晗为大家带来他们在项目期间所做的课题研究。肖文力和林沐晗目前在香港中文大学(深圳)理工学院就读电子与计算机工程专业,曾在 AIRS 进行实习,分别参与联邦学习和机器人相对定位的相关研究。
点击链接报名参加:http://hdxu.cn/ImoyH,或通过Bilibili(http://live.bilibili.com/22587709)、视频号“AIRS研究院”参与。
呼吸新鲜空气,了解前沿科技!AIRS 重磅推出 系列活动 AIRS in the AIR。与您一起探索人工智能与机器人领域的前沿技术、产业应用、发展趋势。
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钱辉环AIRS副院长、香港中文大学(深圳)助理教授执行主席
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肖文力香港中文大学(深圳)本科生Tackling Safe and Efficient Multi-Agent Reinforcement Learning via Dynamic Shielding
Wenli Xiao is a senior majoring in Electrical Information Engineering (Computer Engineering stream) at the Chinese University of Hong Kong, Shenzhen. He has interned at the Robotics Institute Summer Scholar (RISS) at Carnegie Mellon University, where he collaborated with Prof. John Dolan and Yiwei Lyu on safe Multi-Agent Reinforcement Learning. He was also a research assistant at the NCEL Lab at the Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), where he did research in Federated Learning for Robotics and IoT, mentored by Prof. Jianwei Huang and Prof. Bing Luo. His current research interests lie in Autonomous Systems, Robotics, and Reinforcement Learning.
Multi-Agent Reinforcement Learning (MARL) has been increasingly used in safety-critical applications but has no safety guarantees, especially during training. In this paper, we propose dynamic shielding, a novel decentralized MARL framework to ensure safety in both training and deployment phases. Our framework leverages Shield, a reactive system running in parallel with the reinforcement learning algorithm to monitor and correct agents’ behavior. In our algorithm, shields dynamically split and merge according to the environment state in order to maintain decentralization and avoid conservative behaviors while enjoying formal safety guarantees. We demonstrate the effectiveness of MARL with dynamic shielding in the mobile navigation scenario.
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林沐晗香港中文大学(深圳)本科生Less is more: A Robust Visual Inertial Odometry with Active Feature Extraction
Muhan Lin is currently an undergraduate student at the Chinese University of Hong Kong (Shenzhen), majoring in Computer Engineering. Her research interest is in computer vision, localization, sensor data fusion, multi-robot cooperation, and path planning, which are the basis of making robots realize complex but robust group tasks. As a rising researcher in robotics, Muhan cooperated with Dr. Yue Wang on robot-to-robot relative localization at Shenzhen Institute of Artificial Intelligence and Robotics for Society, advised by Prof. Tim Lun Lam. She then extends her research to Visual Odometry and works on this with Shibo Zhao at the AirLab at Carnegie Mellon University, advised by Prof. Sebastian Scherer. She explores pushing the boundary of Visual Inertial Odometry in challenging environments utilizing her experience in relative pose estimation.
To achieve robust performance, it is common for visual odometry and SLAM to track more features, like several hundreds of points in real time. Although this strategy performs well on high-end desktop PCs, it is difficult to apply it to some mobile platforms with limited computation resources, such as VR, Micro UAV, and multi-camera systems. Additionally, noisy visual feature points may decrease the accuracy of visual odometry and SLAM. Therefore, fewer but more informative features can boost efficiency and accuracy compared to extracting more features. It means that less is more. To achieve this target, we propose a new criterion for the active feature selection based on singular values and then incorporate this method into an advanced VIO system, TP-TIO [1]. With the new system, using half of the features required by the original TP-TIO, the residuals can be reduced to 56.23% of the ones generated by the original TP-TIO without increasing the processing time to a large degree. The new system was verified with the mmpug datasets [2], which were extracted in a long and dark corridor.
时间 | 环节 | 嘉宾与题目 |
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11:00-11:20 |
主题报告 |
肖文力,香港中文大学(深圳) |
11:20-11:40 |
主题报告 |
林沐晗,香港中文大学(深圳) |
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