GLiDE：基于质心模型的四足机器人运动
GLiDE: Generalizable Quadrupedal Locomotion in Diverse Environments with a Centroidal Model
摘要：
用于腿部运动的无模型强化学习（RL）通常依赖于物理模拟器，其可以准确地预测机器人的每个自由度的行为。相反，近似降阶模型通常用于许多模型预测控制策略。在这项工作中，我们放弃了在RL中使用高保真动力学模型的传统方法，而是试图了解当将RL与一个更简单的质心模型应用于四足运动时可以实现什么。我们表明，基于RL控制的质心模型的加速度是令人惊讶的有效，当结合二次规划，以实现通过地面接触力的命令的行动。它允许简单的奖励结构、降低的计算成本和鲁棒的仿真到现实迁移。我们通过演示平坦地形步态，踏脚石运动，两条腿的地方平衡，平衡梁运动，并直接仿真到现实迁移的方法的一般性。
英文摘要：
Model-free reinforcement learning (RL) for legged locomotion commonly relies on a physics simulator that can accurately predict the behaviors of every degree of freedom of the robot. In contrast, approximate reduced-order models are commonly used for many model predictive control strategies. In this work we abandon the conventional use of high-fidelity dynamics models in RL and we instead seek to understand what can be achieved when using RL with a much simpler centroidal model when applied to quadrupedal locomotion. We show that RL-based control of the accelerations of a centroidal model is surprisingly effective, when combined with a quadratic program to realize the commanded actions via ground contact forces. It allows for a simple reward structure, reduced computational costs, and robust sim-to-real transfer. We show the generality of the method by demonstrating flat-terrain gaits, stepping-stone locomotion, two-legged in-place balance, balance beam locomotion, and direct sim-to-real transfer.
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