With the research into development of quadruped robots picking up pace, learning based techniques are being explored for developing locomotion controllers for such robots. A key problem is to generate leg trajectories for continuously varying target linear and angular velocities, in a stable manner. In this paper, we propose a two pronged approach to address this problem. First, multiple simpler policies are trained to generate trajectories for a discrete set of target velocities and turning radius. These policies are then augmented using a higher level neural network for handling the transition between the learned trajectories. Specifically, we develop a neural network-based filter that takes in target velocity, radius and transforms them into new commands that enable smooth transitions to the new trajectory. This transformation is achieved by learning from expert demonstrations. An application of this is the transformation of a novice user's input into an expert user's input, thereby ensuring stable manoeuvres regardless of the user's experience. Training our proposed architecture requires much less expert demonstrations compared to standard neural network architectures. Finally, we demonstrate experimentally these results in the in-house quadruped Stoch 2.
Learning Visual Quadrupedal Loco-Manipulation from Demonstrations
Shanghai Qi Zhi Institute（上海期智研究院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of California, Berkeley（加州大学伯克利分校）；Tsinghua University（清华大学）
OmniMimic: Dynamics-completed Motion Augmentation for Multi-style Omnidirectional Quadruped Locomotion
Hunan University（湖南大学）；China Mobile Group Hunan Company Ltd.（中国移动通信集团湖南有限公司）；National Engineering Research Center of Robot Visual Perception and Control Technology（国家机器人视觉感知与控制技术工程研究中心）
Training and Simulation of Quadrupedal Robot in Adaptive Stair Climbing and Descending for Indoor Firefighting: An End-to-End Reinforcement Learning Approach
Independent Researcher（独立研究者）；Department of Construction Management, Western New England University（西雅图新英格兰大学建设管理系）
Design and Control of the "QuadBoat": A Quadruped Surface Vehicle for Drowning Rescue
Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS)（深圳社会人工智能与机器人研究所）；The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen)（香港中文大学（深圳））；Stevens Institute of Technology（史蒂文斯理工学院）
End-to-End Multi-Task Policy Learning from NMPC for Quadruped Locomotion
Institute for Artificial Intelligence and Autonomous Systems (A2S)（人工智能与自主系统研究所）；Bonn-Rhein-Sieg University of Applied Sciences（波恩-莱茵-锡格应用科学大学）；Humanoid Robots Lab（人形机器人实验室）；University of Bonn（波恩大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（Lamarr机器学习与人工智能研究所）；Center for Robotics（机器人中心）；Fraunhofer Institute for Intelligent Analysis and Information Systems（弗劳恩霍夫智能分析与信息系统研究所）
Explosive Jumping with Rigid and Articulated Soft Quadrupeds via Example Guided Reinforcement Learning
Cognitive Robotics, Delft University of Technology（德鲁夫特理工大学认知机器人实验室）；Department of Computer Science, University of Manchester（曼彻斯特大学计算机科学系）；Department of Industrial Engineering, University of Trento（特伦托大学工业工程系）；Institute of Robotics and Mechatronics, German Aerospace Center (DLR)（德国航空航天中心机器人与机电一体化研究所）
Continuous Control of Diverse Skills in Quadruped Robots Without Complete Expert Datasets
Academy for Engineering and Technology, Fudan University（复旦大学工程与技术学院）；Beijing Jingcheng Zhitong Robotics Technology Co.（北京京城智通机器人科技有限公司）；Engineering Research Center of AI and Robotics（AI与机器人工程研究中心）
Offline Imitation Learning upon Arbitrary Demonstrations by Pre-Training Dynamics Representations
School of Engineering and Applied Sciences, Harvard University（哈佛大学工程与应用科学学院）；School of Computational Science and Engineering, Georgia Institute of Technology（佐治亚理工学院计算科学与工程学院）；Mitsubishi Electric Research Laboratories (MERL)（三菱电机研究实验室）；MERL（三菱电机研究实验室）
Safety-Aware Imitation Learning via MPC-Guided Disturbance Injection
Department of Electrical Engineering, Tsinghua University（电子工程系，清华大学）；Department of Electrical and Computer Engineering, University of Southern California（电气与计算机工程系，南加州大学）；Department of Aeronautics and Astronautics, Stanford University（航空与航天工程系，斯坦福大学）
Human-Centered Development of Guide Dog Robots: Quiet and Stable Locomotion Control
Department of Computer Science, University of Texas at Austin（德克萨斯大学奥斯汀分校计算机科学系）；School of Computing and Information Science, University of Maine（缅因大学计算与信息科学学院）
APEX: Action Priors Enable Efficient Exploration for Robust Motion Tracking on Legged Robots
Multi-Agent Robotic Motion Lab（多智能体机器人运动实验室）；Department of Mechanical Engineering, College of Design and Engineering, National University of Singapore（机械工程系，设计与工程学院，新加坡国立大学）；Computational Robotics Lab（计算机器人实验室）；Department of Computer Science, ETH Zurich（计算机科学系，苏黎世联邦理工学院）；Robot Intelligence Lab（机器人智能实验室）；Department of Artificial Intelligence, Korea University（人工智能系，韩国大学）
Towards Tighter Convex Relaxation of Mixed-Integer Programs: Leveraging Logic Network Flow for Task and Motion Planning
George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, USA（乔治亚理工学院机械工程学院）；Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, USA（乔治亚理工学院工业与系统工程学院）
