Trajectory Distribution Control for Model Predictive Path Integral Control using Covariance Steering
Georgia Institute of Technology（佐治亚理工学院）
Ji Yin, Zhiyuan Zhang, Evangelos Theodorou, Panagiotis Tsiotras
备注：
"Previously titled "Improving Model Predictive Path Integral Using Covariance Steering", this paper is one of the Outstanding Planning Paper Award finalists in ICRA 2022. For associated video, see https://www.youtube.com/watch?v=cZq4tnBTIqc ."
This paper presents a novel control approach for autonomous systems operating under uncertainty. We combine Model Predictive Path Integral (MPPI) control with Covariance Steering (CS) theory to obtain a robust controller for general nonlinear systems. The proposed Covariance-Controlled Model Predictive Path Integral (CC-MPPI) controller addresses the performance degradation observed in some MPPI implementations owing to unexpected disturbances and uncertainties. Namely, in cases where the environment changes too fast or the simulated dynamics during the MPPI rollouts do not capture the noise and uncertainty in the actual dynamics, the baseline MPPI implementation may lead to divergence. The proposed CC-MPPI controller avoids divergence by controlling the dispersion of the rollout trajectories at the end of the prediction horizon. Furthermore, the CC-MPPI has adjustable trajectory sampling distributions that can be changed according to the environment to achieve efficient sampling. Numerical examples using a ground vehicle navigating in challenging environments demonstrate the proposed approach.
Operator Splitting Covariance Steering for Safe Stochastic Nonlinear Control
School of Aerospace Engineering, Georgia Institute of Technology（航空航天工程学院，佐治亚理工学院）；School of Electrical and Computer Engineering, Georgia Institute of Technology（电气与计算机工程学院，佐治亚理工学院）
Sensor Query Schedule and Sensor Noise Covariances for Accuracy-constrained Trajectory Estimation
Technical University of Munich（慕尼黑技术大学）；University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究学院）；University of Toronto Robotics Institute（多伦多大学机器人研究所）；Munich Institute of Robotics and Machine Intelligence（慕尼黑机器人与智能机械研究所）；Vector Institute for Artificial Intelligence（人工智能向量研究所）
Stabilizing Humanoid Robot Trajectory Generation via Physics-Informed Learning and Control-Informed Steering
Artificial and Mechanical Intelligence, Italian Institute of Technology（意大利理工学院人工智能与机械智能部门）；Machine Learning and Optimisation, University of Manchester（曼彻斯特大学机器学习与优化部门）；Dipartimento di Automatica e Informatica, Politecnico di Torino（都灵politecnico自动化与信息学院）；Rehab Technologies Lab, Italian Institute of Technology（意大利理工学院康复技术实验室）
Benchmarking Empirical and Learning-Based Approaches for Feedforward Steering Control in Autonomous Racing
Chair of Automatic Control, Department of Engineering Physics and Computation, Technical University of Munich（慕尼黑技术大学自动控制系）；Professorship of Autonomous Vehicle Systems (AVS), Department of Mobility Systems Engineering, Technical University of Munich（慕尼黑技术大学移动系统工程系自动驾驶车辆系统教授职位）
