Reinforcement Learning Compensated Model Predictive Control for Off-road Driving on Unknown Deformable Terrain
Department of Automotive Engineering, Clemson University（汽车工程系，克莱姆森大学）；Ground Vehicle Systems Center, U.S. Army Combat Capabilities Development Command（地面车辆系统中心，美国陆军作战能力发展指挥部）
Prakhar Gupta, Jonathon M. Smereka, Yunyi Jia
备注：
Submitted to IEEE Transactions on Intelligent Vehicles as a Regular Paper; was withdrawn in March 2025. A revised version of this manuscript was submitted to ACC 2025 review as a regular paper in Sep 2025
This study presents an Actor-Critic reinforcement learning Compensated Model Predictive Controller (AC2MPC) designed for high-speed, off-road autonomous driving on deformable terrains. Addressing the difficulty of modeling unknown tire-terrain interaction and ensuring real-time control feasibility and performance, this framework integrates deep reinforcement learning with a model predictive controller to manage unmodeled nonlinear dynamics. We evaluate the controller framework over constant and varying velocity profiles using high-fidelity simulator Project Chrono. Our findings demonstrate that our controller statistically outperforms standalone model-based and learning-based controllers over three unknown terrains that represent sandy deformable track, sandy and rocky track and cohesive clay-like deformable soil track. Despite varied and previously unseen terrain characteristics, this framework generalized well enough to track longitudinal reference speeds with the least error. Furthermore, this framework required significantly less training data compared to purely learning based controller, converging in fewer steps while delivering better performance. Even when under-trained, this controller outperformed the standalone controllers, highlighting its potential for safer and more efficient real-world deployment.
Hybrid Terrain-Aware Path Planning: Integrating VD-RRT* Exploration and VD-D* Lite Repair
The Grainger College of Engineering, Electrical and Computer Engineering Department, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校格拉inger工程学院）；The Grainger College of Engineering, Industrial and Enterprise Systems Engineering Department, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校格拉inger工程学院）
Learning Quadrupedal Locomotion over Challenging Terrain
Robotic Systems Lab, ETH Zurich（苏黎世联邦理工学院机器人系统实验室）；Robotics and Artificial Intelligence Lab, KAIST（韩国科学技术院机器人与人工智能实验室）；Intelligent Systems Lab, Intel（英特尔智能系统实验室）
A Foot Resistive Force Model for Legged Locomotion on Muddy Terrains
Department of Mechanical and Aerospace Engineering, Rutgers University（罗格斯大学机械与航空航天工程系）；Department of Electrical and Biomedical Engineering, University of Nevada（内华达大学电气与生物医学工程系）
Actor-Critic Cooperative Compensation to Model Predictive Control for Off-Road Autonomous Vehicles Under Unknown Dynamics
Department of Automotive Engineering, Clemson University（汽车工程系，克莱姆森大学）；Ground Vehicle Systems Center, U.S. Army Combat Capabilities Development Command（地面车辆系统中心，美国陆军作战能力发展指挥部）
SlipNet: Enhancing Slip Cost Mapping for Autonomous Navigation on Heterogeneous and Deformable Terrains
Khalifa University Center for Autonomous and Robotic Systems (KUCARS)（哈利法大学自主与机器人系统中心）；Advanced Research and Innovation Center (ARIC)（先进研究与创新中心）；Khalifa University（哈利法大学）
VB-Com: Learning Vision-Blind Composite Humanoid Locomotion Against Deficient Perception
Shanghai AI Laboratory（上海人工智能实验室）；The University of Hong Kong（香港大学）；Shanghai Jiao Tong University（上海交通大学）；Zhejiang University（浙江大学）；The Chinese University of Hong Kong（香港中文大学）
TAIL: A Terrain-Aware Multi-Modal SLAM Dataset for Robot Locomotion in Deformable Granular Environments
Southern University of Science and Technology (SUSTech)（南方科技大学）；Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems（深圳市仿生机器人与智能系统重点实验室）；Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities（广东省高校人机增强与康复机器人重点实验室）
Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information
Space Robotics Lab., Dept. Aerospace Eng., Tohoku University, Japan（东京东京大学航空航天工程系空间机器人实验室，日本）；Dept. of Information Technology and Human Factors, the National Institute of Advanced Industrial Science and Technology, Japan（日本国家先进工业科学和技术研究院信息科技与人因系）
An adaptive hierarchical control framework for quadrupedal robots in planetary exploration
Robotics Innovation Center, DFKI GmbH（德意志联邦共和国家际机器人创新中心，DFKI GmbH）；German Aerospace Center, Institute of Space Systems（德国航空航天中心，空间系统研究所）；University of Bremen（不莱梅大学）
