Many robotic tasks require high-dimensional sensors such as cameras and Lidar to navigate complex environments, but developing certifiably safe feedback controllers around these sensors remains a challenging open problem, particularly when learning is involved. Previous works have proved the safety of perception-feedback controllers by separating the perception and control subsystems and making strong assumptions on the abilities of the perception subsystem. In this work, we introduce a novel learning-enabled perception-feedback hybrid controller, where we use Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) to show the safety and liveness of a full-stack perception-feedback controller. We use neural networks to learn a CBF and CLF for the full-stack system directly in the observation space of the robot, without the need to assume a separate perception-based state estimator. Our hybrid controller, called LOCUS (Learning-enabled Observation-feedback Control Using Switching), can safely navigate unknown environments, consistently reach its goal, and generalizes safely to environments outside of the training dataset. We demonstrate LOCUS in experiments both in simulation and in hardware, where it successfully navigates a changing environment using feedback from a Lidar sensor.
GSAM: A Generalizable and Safe Robotic Framework for Articulated Object Manipulation
College of Computer Science, Chongqing University, Chongqing, China（重庆大学计算机学院）；Lumos Robotics, China（Lumos机器人中国）；Xi'an Jiaotong-Liverpool University, China（西安交通大学利物浦大学）；Fudan University, China（复旦大学）；Department of Information Engineering and Computer Science, University of Trento, Trento, Italy（特伦托大学信息工程与计算机科学系）
SAGE-LLM: Towards Safe and Generalizable LLM Controller with Fuzzy-CBF Verification and Graph-Structured Knowledge Retrieval for UAV Decision
School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an 710072, China（人工智能学院、光学与电子学（iOPEN）、西北工业大学，西安 710072，中国）；Institute of Artificial Intelligence (TeleAI), China Telecom, China（人工智能研究所（TeleAI）、中国电信，中国）
SEG-Parking: Towards Safe, Efficient, and Generalizable Autonomous Parking via End-to-End Offline Reinforcement Learning
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Cheng Kar-Shun Robotics Institute, The Hong Kong University of Science and Technology（陈克逊机器人研究所，香港科技大学）
MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning
ETH Zurich（苏黎世联邦理工学院）；The Chinese University of Hong Kong（香港中文大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Centre for Perceptual and Interactive Intelligence（感知与交互智能研究中心）
