Environmental Adaptation of Robot Morphology and Control through Real-world Evolution
University of Oslo（奥斯陆大学）；Norwegian Defence Research Establishment（挪威国防研究机构）；Australian National University（澳大利亚国立大学）；CSIRO（澳大利亚联邦科学与工业研究组织）；RITMO（节奏研究中心）
Tønnes F. Nygaard, Charles P. Martin, David Howard, Jim Torresen, Kyrre Glette
Robots operating in the real world will experience a range of different environments and tasks. It is essential for the robot to have the ability to adapt to its surroundings to work efficiently in changing conditions. Evolutionary robotics aims to solve this by optimizing both the control and body (morphology) of a robot, allowing adaptation to internal, as well as external factors. Most work in this field has been done in physics simulators, which are relatively simple and not able to replicate the richness of interactions found in the real world. Solutions that rely on the complex interplay between control, body, and environment are therefore rarely found. In this paper, we rely solely on real-world evaluations and apply evolutionary search to yield combinations of morphology and control for our mechanically self-reconfiguring quadruped robot. We evolve solutions on two distinct physical surfaces and analyze the results in terms of both control and morphology. We then transition to two previously unseen surfaces to demonstrate the generality of our method. We find that the evolutionary search finds high-performing and diverse morphology-controller configurations by adapting both control and body to the different properties of the physical environments. We additionally find that morphology and control vary with statistical significance between the environments. Moreover, we observe that our method allows for morphology and control parameters to transfer to previously-unseen terrains, demonstrating the generality of our approach.
Efficient Morphology-Control Co-Design via Stackelberg Proximal Policy Optimization
Center of Excellence for Generative AI, King Abdullah University of Science and Technology (KAUST)（生成人工智能卓越中心，卡奥斯特大学）；Dalle Molle Institute for Artificial Intelligence Research (IDSIA)（人工智能研究达勒莫利研究所）；Università della Svizzera italiana (USI)（瑞士意大利大学）；Scuola universitaria professionale della Svizzera italiana (SUPSI)（瑞士意大利专业大学）
MorphIt: Flexible Spherical Approximation of Robot Morphology for Representation-driven Adaptation
Human Interaction and RObotics [HIRO] Group（人机交互与机器人[HIRO]组）；Department of Computer Science（计算机科学系）；College of Engineering and Applied Science（工程与应用科学学院）；University of Colorado Boulder（科罗拉多大学波尔得分校）
Context-Aware Force Estimation for Deformable Tool Manipulation in Robotic Environmental Swabbing via Few-Shot Continual Adaptation
Department of Biological and Agricultural Engineering, University of Arkansas（阿肯色大学，生物与农业工程系）；Department of Food Science, University of Arkansas（阿肯色大学，食品科学系）
Adaptive-LIO: Enhancing Robustness and Precision through Environmental Adaptation in LiDAR Inertial Odometry
State Key Laboratory of Robotics and Systems（机器人学与系统国家重点实验室）；Harbin Institute of Technology（哈尔滨工业大学）；Hangzhou Qisheng Intelligent Techology Co. Ltd.（杭州齐盛智能科技有限公司）；School of Mechatronic Engineering, China University of Mining and Technology（中国矿业大学机电工程学院）；School of Mechanical and Electrical Engineering, University of Electronic Science And Technology（电子科技大学机械与电气工程学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）
Department of Computer Science, Stanford University（斯坦福大学计算机科学系）；Department of Applied Physics, Stanford University（斯坦福大学应用物理系）；Wu-Tsai Neurosciences Institute, Stanford University（斯坦福大学吴蔡神经科学研究所）；Stanford Institute for Human-Centered Artificial Intelligence, Stanford University（斯坦福大学以人为本人工智能研究院）
Bumper Drone: Elastic Morphology Design for Aerial Physical Interaction
University of Bristol（布里斯托大学）；School of Engineering Mathematics and Technology（工程数学与技术学院）；School of Civil, Aerospace and Design Engineering（土木、航空航天与设计工程学院）；Department of Aeronautics（航空系）；Laboratory of Sustainability Robotics, EMPA（可持续机器人实验室，EMPA）
Few-Shot Adaptation to Non-Stationary Environments via Latent Trend Embedding for Robotics
College of Information Science and Engineering, Ritsumeikan University（立命馆大学信息科学与工程学院）；Production & Technology Department, NIPPN CORPORATION（NIPPN公司生产与技术部门）；University of Osaka（大阪大学）；National Institute of Technology, Ichinoseki College（国立Ichinoseki学院）
The Adaptive Architectural Layout: How the Control of a Semi-Autonomous Mobile Robotic Partition was Shared to Mediate the Environmental Demands and Resources of an Open-Plan Office
Distributed Recurrent Neural Forward Models with Synaptic Adaptation for Complex Behaviors of Walking Robots
Institute for Physics - Biophysics, George-Agust-University（乔治-奥古斯特大学物理-生物物理研究所）；Bernstein Center for Computational Neuroscience, George-Agust-University（乔治-奥古斯特大学伯恩斯坦计算神经科学中心）；Riken Brain Science Institute（理化学研究所脑科学研究所）；Maersk Mc-Kinney Moller Institute, University of Southern Denmark（南丹麦大学马士基·麦金尼·穆勒研究所）
Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects
NVIDIA（英伟达）；University of California, Berkeley（加州大学伯克利分校）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）；Caltech（加州理工学院）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
