通过在线学习的自适应四肢运动控制
Adaptive Legged Locomotion via Online Learning for Model Predictive Control
AI总结：
该研究提出了一种通过在线学习和模型预测控制实现自适应四肢运动的算法，能够处理未知负载和不规则地形下的复杂任务。
摘要：
我们提供了一种通过在线学习和模型预测控制实现自适应四肢运动的算法。该算法由两个相互作用的模块组成：模型预测控制（MPC）和残差动力学的在线学习。残差动力学可以表示建模误差和外部扰动。我们受到未来自主性发展的启发，其中四足机器人将在现实世界中未知的不确定性下自主执行复杂任务，例如未知负载和不规则地形。该算法使用随机傅里叶特征来近似残差动力学在再生产核希尔伯特空间中的表示。然后，它基于当前学习的残差动力学模型使用MPC。该模型通过在线自监督方式使用最小二乘法更新，基于在控制四足机器人时收集的数据。该算法具有亚线性动态遗憾，定义为相对于一个最优的clairvoyant控制器的次优性能，该控制器知道残差动力学如何变化。我们在Gazebo和MuJoCo仿真中验证了我们的算法，其中四足机器人旨在跟踪参考轨迹。Gazebo仿真包括持续未知的外部力，最大达到12g，其中g是重力矢量，在平坦地形、20度倾斜地形和有0.25米高度变化的粗糙地形中。MuJoCo仿真包括时间变化的未知扰动，负载最大达到8kg，以及时间变化的地面摩擦系数在平坦地形中。
英文摘要：
We provide an algorithm for adaptive legged locomotion via online learning and model predictive control. The algorithm is composed of two interacting modules: model predictive control (MPC) and online learning of residual dynamics. The residual dynamics can represent modeling errors and external disturbances. We are motivated by the future of autonomy where quadrupeds will autonomously perform complex tasks despite real-world unknown uncertainty, such as unknown payload and uneven terrains. The algorithm uses random Fourier features to approximate the residual dynamics in reproducing kernel Hilbert spaces. Then, it employs MPC based on the current learned model of the residual dynamics. The model is updated online in a self-supervised manner using least squares based on the data collected while controlling the quadruped. The algorithm enjoys sublinear \textit{dynamic regret}, defined as the suboptimality against an optimal clairvoyant controller that knows how the residual dynamics. We validate our algorithm in Gazebo and MuJoCo simulations, where the quadruped aims to track reference trajectories. The Gazebo simulations include constant unknown external forces up to $12\boldsymbol{g}$, where $\boldsymbol{g}$ is the gravity vector, in flat terrain, slope terrain with $20\degree$ inclination, and rough terrain with $0.25m$ height variation. The MuJoCo simulations include time-varying unknown disturbances with payload up to $8~kg$ and time-varying ground friction coefficients in flat terrain.
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