Data-driven learning based methods have recently been particularly successful at learning robust locomotion controllers for a variety of unstructured terrains. Prior work has shown that incorporating good locomotion priors in the form of trajectory generators (TGs) is effective at efficiently learning complex locomotion skills. However, defining a good, single TG as tasks/environments become increasingly more complex remains a challenging problem as it requires extensive tuning and risks reducing the effectiveness of the prior. In this paper, we present Evolved Environmental Trajectory Generators (EETG), a method that learns a diverse set of specialised locomotion priors using Quality-Diversity algorithms while maintaining a single policy within the Policies Modulating TG (PMTG) architecture. The results demonstrate that EETG enables a quadruped robot to successfully traverse a wide range of environments, such as slopes, stairs, rough terrain, and balance beams. Our experiments show that learning a diverse set of specialized TG priors is significantly (5 times) more efficient than using a single, fixed prior when dealing with a wide range of environments.
Evolving soft locomotion in aquatic and terrestrial environments: effects of material properties and environmental transitions
Morphology, Evolution & Cognition Lab, University of Vermont（佛蒙特大学形态、进化与认知实验室）；Department of Biological Statistics and Computational Biology, Cornell University（康奈尔大学生物统计与计算生物学系）；Fluid Structure Interaction Research Group, Southampton Marine and Maritime Institute, University of Southampton（南安普顿大学南安普顿海洋与海事研究所流体结构相互作用研究组）
Learning and Adapting Agile Locomotion Skills by Transferring Experience
UC Berkeley（加州大学伯克利分校）；Berkeley AI Research（伯克利人工智能研究院）；Google Research（谷歌研究院）；Georgia Institute of Technology（佐治亚理工学院）；Simon Fraser University（西蒙菲莎大学）
Learning to Walk in the Real World with Minimal Human Effort
Robotics at Google（谷歌机器人学）；Berkeley Artificial Intelligence Research, University of California, Berkeley（加州大学伯克利分校伯克利人工智能研究）；Georgia Institute of Technology（佐治亚理工学院）
StyleLoco: Generative Adversarial Distillation for Natural Humanoid Robot Locomotion
National Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能国家重点实验室，BIGAI）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；Huazhong University of Science and Technology（华中科技大学）
Teacher Motion Priors: Enhancing Robot Locomotion over Challenging Terrain
University of Chinese Academy of Sciences（中国科学院大学）；Chinese Academy of Sciences（中国科学院）；Beijing Zhongke Huiling Robot Technology Co., LTD（北京中科创联机器人技术有限公司）
Time to Play: Simulating Early-Life Animal Dynamics Enhances Robotics Locomotion Discovery
Adaptive & Intelligent Robotics Lab, Imperial College London, United Kingdom（智能适应机器人实验室，帝国理工学院伦敦分校）；Evolutionary Biomechanics Laboratory, Imperial College London, United Kingdom（进化生物力学实验室，帝国理工学院伦敦分校）
