Real-time Digital Double Framework to Predict Collapsible Terrains for Legged Robots
National University of Singapore (NUS)（新加坡国立大学）；Institute for Infocomm Research (I 2 R)（信息通信研究所）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）
Garen Haddeler, Hari P. Palanivelu, Yung Chuen Ng, Fabien Colonnier, Albertus H. Adiwahono, Zhibin Li, Chee-Meng Chew, Meng Yee Michael Chuah
Inspired by the digital twinning systems, a novel real-time digital double framework is developed to enhance robot perception of the terrain conditions. Based on the very same physical model and motion control, this work exploits the use of such simulated digital double synchronized with a real robot to capture and extract discrepancy information between the two systems, which provides high dimensional cues in multiple physical quantities to represent differences between the modelled and the real world. Soft, non-rigid terrains cause common failures in legged locomotion, whereby visual perception solely is insufficient in estimating such physical properties of terrains. We used digital double to develop the estimation of the collapsibility, which addressed this issue through physical interactions during dynamic walking. The discrepancy in sensory measurements between the real robot and its digital double are used as input of a learning-based algorithm for terrain collapsibility analysis. Although trained only in simulation, the learned model can perform collapsibility estimation successfully in both simulation and real world. Our evaluation of results showed the generalization to different scenarios and the advantages of the digital double to reliably detect nuances in ground conditions.
Istituto Italiano di Tecnologia（意大利技术研究院）；Università degli Studi di Genova（热那亚大学）；Swiss Federal Institute of Technology at Lausanne（洛桑联邦理工学院）；University of Texas（德克萨斯大学）；University of Naples Federico II（那不勒斯费德里科二世大学）
ViTAL: Vision-Based Terrain-Aware Locomotion for Legged Robots
Istituto Italiano di Tecnologia (IIT)（意大利理工学院）
这项工作是基于视觉的规划策略的足式机器人分离到立足点选择和姿势适应运动规划。目前的姿态自适应策略优化机器人的身体姿势相对于给定的立足点。如果未到达这些立足点，则机器人可能最终处于没有可到达的安全立足点的状态。因此，我们提出了一个基于视觉的地形感知运动（ViTAL）的战略，包括新的姿态适应和立足点选择算法。ViTAL在姿势适应中引入了一种不同的范例，该范例不优化相对于给定立足点的身体姿势，而是最大化腿部到达安全立足点的机会的身体姿势。ViTAL根据机器人的能力和地形感知能力来规划立足点和姿势。我们使用90 kg HyQ和140 kg HyQReal四足机器人来验证ViTAL，并表明它们能够以不同的速度和步态爬上各种障碍物，包括楼梯，间隙和粗糙的地形。我们比较ViTAL与基线策略，选择机器人姿势的基础上，给定选定的立足点，并显示ViTAL优于基线。
PIE: Parkour with Implicit-Explicit Learning Framework for Legged Robots
Institute of Cyber-Systems and Control, Zhejiang University（浙江大学控制科学与工程学院 cyber-system 研究所）；State Key Laboratory of Industrial Control Technology（工业控制技术国家重点实验室）
FZI Research Center for Information Technology（弗劳恩霍夫研究所信息技术研究中心）；Machine Intelligence and Robotics Lab at the Karlsruhe Institute for Technology (KIT)（卡尔斯鲁厄理工学院机械智能与机器人实验室）
ATRos: Learning Energy-Efficient Agile Locomotion for Wheeled-legged Robots
Shanghai Huawei Technologies Co., Ltd.（上海华为技术有限公司）；College of Future Information Technology, Fudan University（复旦大学未来信息科技学院）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；School of Aeronautic and Astronautics, Zhejiang University（浙江大学航空宇航学院）；School of Electronics and Communication Engineering, Sun Yat-sen University（中山大学电子与通信工程学院）
SlipSense: Multimodal Sensing for Online Slip Detection in Legged Robots
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Institute for Infocomm Research, Agency for Science Technology and Research（资讯通信研究院，科技研究局）
Real-time Neural Dense Elevation Mapping for Urban Terrain with Uncertainty Estimations
Hong Kong University of Science and Technology（香港科技大学）；Clear Water Bay Institute of Autonomous Driving（清水湾自动驾驶研究院）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳研究院）
Terrain-Aware Quadrupedal Locomotion via Reinforcement Learning
Tencent Robotics X（腾讯Robotics X）；The Chinese University of Hong Kong（香港中文大学）；Southern University of Science and Technology（南方科技大学）；University of Alberta（阿尔伯塔大学）
Legged Autonomous Surface Science In Analogue Environments (LASSIE): Making Every Robotic Step Count in Planetary Exploration
Collaborative Robotics and Intelligent Systems Institute, Oregon State University（协作机器人与智能系统研究所，俄勒冈州立大学）；Department of Geology and Geophysics, Texas A&M University（地质与地球物理系，德克萨斯大学）；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 Earth and Environmental Science, University of Pennsylvania（地球与环境科学系，宾夕法尼亚大学）；Department of Astronomy and Planetary Science, Northern Arizona University（天文学与行星科学系，北方亚利桑那大学）；School of Earth and Atmospheric Sciences, Georgia Institute of Technology（地球与大气科学学院，佐治亚理工学院）；NASA Marshall Space Flight Center（美国国家航空航天局马歇尔航天飞行中心）；NASA Johnson Space Center（美国国家航空航天局约翰逊航天中心）；Department of Electrical and Systems Engineering, University of Pennsylvania（电气与系统工程系，宾夕法尼亚大学）；Department of Psychology, Temple University（心理学系， Temple 大学）
PrePARE: Predictive Proprioception for Agile Failure Event Detection in Robotic Exploration of Extreme Terrains
NASA Jet Propulsion Laboratory, California Institute of Technology（美国国家航空航天局喷气推进实验室，加州理工学院）；University of Goettingen, University Medical Center Goettingen（哥廷根大学，哥廷根大学医学中心）；Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（佐治亚理工学院机器人与智能机器研究所）；Swiss Federal Institute of Technology (ETH Zürich)（苏黎世联邦理工学院）；California Institute of Technology（加州理工学院）
Learning to enhance multi-legged robot on rugged landscapes
Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（佐治亚理工学院机器人与智能机器研究所）；School of Physics, Georgia Institute of Technology（佐治亚理工学院物理学院）；School of Computing, Georgia Institute of Technology（佐治亚理工学院计算学院）
PAINT: Partner-Agnostic Intent-Aware Cooperative Transport with Legged Robots
Robotic Systems Lab, ETH Zurich, Switzerland（瑞士苏黎世联邦理工学院机器人系统实验室）；Computational Robotics Lab, ETH Zurich, Switzerland（瑞士苏黎世联邦理工学院计算机器人学实验室）；ETH AI Center, ETH Zurich, Switzerland（瑞士苏黎世联邦理工学院ETH人工智能中心）
