This paper presents a novel contact-implicit trajectory optimization method using an analytically solvable contact model to enable planning of interactions with hard, soft, and slippery environments. Specifically, we propose a novel contact model that can be computed in closed-form, satisfies friction cone constraints and can be embedded into direct trajectory optimization frameworks without complementarity constraints. The closed-form solution decouples the computation of the contact forces from other actuation forces and this property is used to formulate a minimal direct optimization problem expressed with configuration variables only. Our simulation study demonstrates the advantages over the rigid contact model and a trajectory optimization approach based on complementarity constraints. The proposed model enables physics-based optimization for a wide range of interactions with hard, slippery, and soft grounds in a unified manner expressed by two parameters only. By computing trotting and jumping motions for a quadruped robot, the proposed optimization demonstrates the versatility for multi-contact motion planning on surfaces with different physical properties.
Zero-Shot Retargeting of Learned Quadruped Locomotion Policies Using Hybrid Kinodynamic Model Predictive Control
University of Notre Dame（圣母大学）；Robotics at Google（谷歌机器人部门）
强化学习（RL）在四足动物运动方面取得了长足的进步，并在策略的可靠仿真到现实迁移方面取得了持续的进展。然而，在另一个机器人上重用策略仍然是一个挑战，这可以节省重新训练的时间。在这项工作中，我们提出了一个框架zero-shot政策重定向，其中不同的运动技能可以在不同形状和大小的机器人之间转移。新的框架集中在一个规划和控制管道，系统地集成RL和模型预测控制（MPC）。规划阶段采用RL生成动态似然轨迹以及接触时间表，避免了接触序列优化的组合复杂性。然后，这些信息被用于播种MPC，以通过一个新的混合Kinodynamic（HKD）模型来稳定和鲁棒化政策推出，该模型隐式优化了立足点位置。硬件结果表明，能够将策略从A1和Laikago机器人传输到MIT Mini Cheetah机器人，而无需任何策略重新调整。
IMPACT: An Implicit Active-Set Augmented Lagrangian for Fast Contact-Implicit Trajectory Optimization
PEARL Lab, Dept. of Computer Science, TU Darmstadt, Germany（图腾实验室，计算机科学系，图恩大学，德国）；Hessian.AI, Darmstadt, Germany（海森AI，图恩，德国）；Robotics Institute Germany (RIG)（德国机器人研究所（RIG））
MA-ROESL: Motion-aware Rapid Reward Optimization for Efficient Robot Skill Learning from Single Videos
College of Information Science and Technology, Eastern Institute of Technology（信息科学与技术学院，东部技术学院）；School of Computer Science and Technology, The University of Science and Technology of China（计算机科学与技术学院，中国科学技术大学）；Department of Computing, The Hong Kong Polytechnic University（计算学院，香港理工大学）
Guiding Energy-Efficient Locomotion through Impact Mitigation Rewards
Department of Electrical and Computer Engineering, Northeastern University（电气与计算机工程系，东北大学）；Department of Aerospace Engineering, California Institute of Technology（航空航天工程系，加州理工学院）
Human Locomotion Implicit Modeling Based Real-Time Gait Phase Estimation
Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education（生物力学与机械生物学重点实验室（北京航空航天大学）教育部）；Key Laboratory of Innovation and Transformation of Advanced Medical Devices, Ministry of Industry and Information Technology（先进医疗设备创新与转化重点实验室（工业和信息化部））；National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering)（先进医疗设备产业教育融合国家级创新平台（医学与工程交叉学科））；School of Biological Science and Medical Engineering, Beihang University（生物科学与医学工程学院（北京航空航天大学））
End-to-End Reinforcement Learning for Torque Based Variable Height Hopping
Indian Institute of Technology (Banaras Hindu University)（印度理工学院（Banaras Hindu大学））；DFKI GmbH Robotics Innovation Center（德国人工智能研究中心机器人创新中心）；Kyoto University（京都大学）
