学习仿人机器人在离散地形上的通用与动态运动
Learning Generic and Dynamic Locomotion of Humanoids Across Discrete Terrains
AI总结：
本文提出一种结合RL训练的神经网络策略与MPC及WBIC控制器的仿人机器人运动架构，在离散地形上实现动态运动决策，显著减少训练样本需求并具备跨平台迁移能力。
摘要：
本文探讨了仿人机器人地形自适应动态运动的挑战，该问题传统上由基于优化的方法或强化学习（RL）解决。基于优化的方法，如模型预测控制（MPC），擅长寻找最优反作用力并实现敏捷运动，尤其在四足机器人中，但难以处理腿式系统的非线性混合动力学以及步位置、时序和反作用的实时计算。相反，基于RL的方法在导航动态和崎岖地形方面显示出潜力，但受限于其庞大的数据需求。我们提出了一种新颖的运动架构，将通过RL在简化环境中训练的神经网络策略，与结合了MPC和全身冲量控制（WBIC）的最先进运动控制器相集成。该策略无需完整动力学仿真即可高效学习高级运动策略，如步态选择和步定位。这种控制架构使仿人机器人能够动态导航离散地形，基于地面高度图做出战略性运动决策（例如，行走、跳跃和跨跃）。我们的结果表明，这种集成控制架构实现了动态运动，所需训练样本显著少于传统基于RL的方法，且可迁移至不同仿人平台而无需额外训练。该控制架构已在动态仿真中进行了广泛测试，为三种不同机器人完成了基于地形高度的动态运动。
英文摘要：
This paper addresses the challenge of terrain-adaptive dynamic locomotion in humanoid robots, a problem traditionally tackled by optimization-based methods or reinforcement learning (RL). Optimization-based methods, such as model-predictive control, excel in finding optimal reaction forces and achieving agile locomotion, especially in quadruped, but struggle with the nonlinear hybrid dynamics of legged systems and the real-time computation of step location, timing, and reaction forces. Conversely, RL-based methods show promise in navigating dynamic and rough terrains but are limited by their extensive data requirements. We introduce a novel locomotion architecture that integrates a neural network policy, trained through RL in simplified environments, with a state-of-the-art motion controller combining model-predictive control (MPC) and whole-body impulse control (WBIC). The policy efficiently learns high-level locomotion strategies, such as gait selection and step positioning, without the need for full dynamics simulations. This control architecture enables humanoid robots to dynamically navigate discrete terrains, making strategic locomotion decisions (e.g., walking, jumping, and leaping) based on ground height maps. Our results demonstrate that this integrated control architecture achieves dynamic locomotion with significantly fewer training samples than conventional RL-based methods and can be transferred to different humanoid platforms without additional training. The control architecture has been extensively tested in dynamic simulations, accomplishing terrain height-based dynamic locomotion for three different robots.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
