Humanoid robots maintain balance and navigate by controlling the contact wrenches applied to the environment. While it is possible to plan dynamically-feasible motion that applies appropriate wrenches using existing methods, a humanoid may also be affected by external disturbances. Existing systems typically rely on controllers to reactively recover from disturbances. However, such controllers may fail when the robot cannot reach contacts capable of rejecting a given disturbance. In this paper, we propose a search-based footstep planner which aims to maximize the probability of the robot successfully reaching the goal without falling as a result of a disturbance. The planner considers not only the poses of the planned contact sequence, but also alternative contacts near the planned contact sequence that can be used to recover from external disturbances. Although this additional consideration significantly increases the computation load, we train neural networks to efficiently predict multi-contact zero-step and one-step capturability, which allows the planner to generate robust contact sequences efficiently. Our results show that our approach generates footstep sequences that are more robust to external disturbances than a conventional footstep planner in four challenging scenarios.
Humanoid Loco-manipulation Planning based on Graph Search and Reachability Maps
CNRS-AIST JRL (Joint Robotics Laboratory)（CNRS-AIST联合机器人实验室）；IRL（机器人研究所）；National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究院）
Learning Differentiable Reachability Maps for Optimization-based Humanoid Motion Generation
CNRS-AIST JRL (Joint Robotics Laboratory)（法国国家科学研究中心-日本产业技术综合研究所联合机器人实验室）；IRL（智能机器人实验室）；National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究所）
Humanoid Path Planning over Rough Terrain using Traversability Assessment
Institute of Human and Machine Cognition（人类与机器认知研究所）；University of West Florida（西佛罗里达大学）；Pusan National University（釜山国立大学）；Boardwalk Robotics, Inc.（Boardwalk机器人公司）
我们提出了一个规划框架，旨在人形导航具有挑战性的地形。该框架旨在使用2.5D高度图规划可遍历，平滑和无碰撞的路径。规划器由两个阶段组成。第一阶段由A* 规划器组成，该规划器使用地形特征对可通行性进行推理。提出了一种新的成本函数，它编码的双足步态直接到图形结构，使自然的路径，是强大的小差距的可通行性。第二阶段是一个优化框架，在进一步提高可遍历性的同时平滑路径。该规划器在模拟中的各种地形上进行了测试，并与脚步规划器和平衡控制器相结合，以创建一个集成的导航框架，该框架在DRC Boston Dynamics Atlas机器人上进行了演示。
Towards Proprioception-Aware Embodied Planning for Dual-Arm Humanoid Robots
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；AgiBot；School of Computer Science, Peking University（北京大学计算机学院）
Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning
Georgia Institute of Technology（佐治亚理工学院）；The University of Southern California（南加州大学）；Technische Universität München（慕尼黑技术大学）；Google DeepMind（谷歌DeepMind）；The AI Institute（人工智能研究所）；The Institute for Human and Machine Cognition（人机认知研究所）；Duke University（杜克大学）；Kuwait University（科威特大学）；Stanford University（斯坦福大学）；CNRS-University of Montpellier（国家科学研究中心-蒙彼利埃大学）；LIRMM CNRS-AIST Joint Robotics Laboratory（联合机器人实验室）；Simon Fraser University（西蒙弗雷泽大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；NVIDIA（英伟达）；Carnegie Mellon University（卡内基梅隆大学）；Harbin Institute of Technology（哈尔滨工业大学）
RAVEN: Reinforcement-Adaptive Visibility-Graph Planning for Robust Humanoid Navigation with Collision-Free MPC
Robotics and Mechanisms Laboratory (RoMeLa), Department of Mechanical and Aerospace Engineering, University of California, Los Angeles（机器人与机构实验室（RoMeLa），机械与航天工程系，加利福尼亚大学洛杉矶分校）
DoublyAware: Dual Planning and Policy Awareness for Temporal Difference Learning in Humanoid Locomotion
University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Intelligent Autonomous Systems Lab（智能自主系统实验室）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Automation & Control Institute (ACIN)（自动化与控制研究所）；Austrian Institute of Technology (AIT) GmbH（奥地利技术研究所）
iWalker: Imperative Visual Planning for Walking Humanoid Robot
The Spatial AI & Robotics (SAIR) Lab, Computer Science and Engineering, University at Buffalo（布法罗大学空间人工智能与机器人实验室）；The Wisconsin Expeditious Legged Locomotion (WELL) Lab, Mechanical Engineering, University of Wisconsin-Madison（威斯康星大学麦迪逊分校快速腿部运动实验室）
Path and Motion Optimization for Efficient Multi-Location Inspection with Humanoid Robots
State Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence (BIGAI), Beijing（国家通用人工智能重点实验室，北京通用人工智能研究院（BIGAI））；Department of Automation, Tsinghua University（自动化系，清华大学）；Department of Automation, University of Science and Technology of China（自动化系，中国科学技术大学）；Department of Computer Science, Harbin Institute of Technology（计算机科学系，哈尔滨工业大学）；Institute for Artificial Intelligence and School of Artificial Intelligence, Peking University（人工智能研究院和人工智能学院，北京大学）
PolygMap: A Perceptive Locomotion Framework for Humanoid Robot Stair Climbing
The Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人社会研究院）；Electronic science and technology, Guangdong University of Technology（广东技术大学电子科学与技术学院）；Leju robotics（乐居机器人）
TD-GRPC: Temporal Difference Learning with Group Relative Policy Constraint for Humanoid Locomotion
University of Texas at Arlington（德克萨斯大学阿灵顿分校）；VinRobotics；Intelligent Autonomous Systems Lab, TU Darmstadt（图宾根大学智能自主系统实验室）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Automation & Control Institute (ACIN), TU Wien（维也纳技术大学自动化与控制研究所）
