We represent a vehicle dynamics model for autonomous driving near the limits of handling via a multi-layer neural network. Online adaptation is desirable in order to address unseen environments. However, the model needs to adapt to new environments without forgetting previously encountered ones. In this study, we apply Continual-MAML to overcome this difficulty. It enables the model to adapt to the previously encountered environments quickly and efficiently by starting updates from optimized initial parameters. We evaluate the impact of online model adaptation with respect to inference performance and impact on control performance of a model predictive path integral (MPPI) controller using the TRIKart platform. The neural network was pre-trained using driving data collected in our test environment, and experiments for online adaptation were executed on multiple different road conditions not contained in the training data. Empirical results show that the model using Continual-MAML outperforms the fixed model and the model using gradient descent in test set loss and online tracking performance of MPPI.
Delft University of Technology（代尔夫特理工大学）；University Libre du Bruxelles（布鲁塞尔自由大学）；Fraunhofer Institute for Manufacturing Engineering and Automation IPA（弗劳恩霍夫制造工程与自动化研究所IPA）
自适应可以用于机器人技术，以提高系统的鲁棒性和可靠性。这项工作描述了机器人自适应的元控制方法。特别是，它详细介绍了MROS（Metacontrol for ROS Systems）框架如何实现和封装Metacontrol，并演示了MROS如何应用于移动机器人在工厂车间导航的导航场景。视频：https://www.youtube.com/watch? v=ISe9aMskJuE
Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving
Jet Propulsion Laboratory, California Institute of Technology（喷气推进实验室、加州理工学院）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Georgia Institute of Technology（佐治亚理工学院）
Knowledge-Distilled End-to-End Reinforcement Learning for Smooth 6-DOF Thrust Control and Rapid Adaptation to Ocean Currents in Remotely Operated Vehicles
Northwestern Polytechnical University（西北工业大学）；Wuhan Second Ship Design and Research Institute（武汉第二船舶设计研究院）
Meta-Learning for Fast Adaptation in Intent Inferral on a Robotic Hand Orthosis for Stroke
Department of Mechanical Engineering, Columbia University in the City of New York（机械工程系，哥伦比亚大学（纽约市））；Department of Computer Science, Columbia University in the City of New York（计算机科学系，哥伦比亚大学（纽约市））；Department of Rehabilitation and Regenerative Medicine, Columbia University Irving Medical Center（康复与再生医学系，哥伦比亚大学伊万斯医学中心）
First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handling
Toyota Research Institute（丰田研究院）；Department of Mechanical Engineering, Rice University（机械工程系， Rice大学）；MANE, Rensselaer Polytechnic Institute（MANE，Rensselaer理工学院）
针对极限动态驾驶中在线学习滞后易致失控的问题，提出结合贝叶斯最后一层元学习与不确定性引导主动数据收集的车辆动力学辨识框架，并在 Toyota Supra 漂移实验中验证其可靠控制效果。
Safe Navigation under State Uncertainty: Online Adaptation for Robust Control Barrier Functions
Department of Mechanical and Civil Engineering, California Institute of Technology（机械与土木工程系，加州理工学院）；Electrical and Computer Engineering Department, University of California at Los Angeles（电气与计算机工程系，加州大学洛杉矶分校）
