论GPU基于样本的随机预测控制器在足式运动中的优势
On the Benefits of GPU Sample-Based Stochastic Predictive Controllers for Legged Locomotion
AI总结：
本文提出一种基于GPU加速的样本随机控制策略用于四足机器人，以实现步态频率适应。实验表明，该方法在中等干扰下与传统MPC相当，并在持续外部干扰下显著优于基于梯度的方法。
摘要：
四足机器人在机动性方面表现出色，能够敏捷地穿越复杂地形。然而，其复杂的控制系统带来了仍远未被完全解决的挑战。本文引入了基于样本的随机控制策略用于四足机器人，作为传统最优控制律的替代方案。我们展示了在GPU加速的支持下，基于样本的随机方法可以有效地应用于真实的四足机器人。特别是，在本工作中，我们专注于实现步态频率适应，这是基于梯度方法在四足运动中的一个显著挑战。为了验证基于样本的随机控制器的有效性，我们测试了两种用于四足机器人的不同方法，并将它们与传统的基于梯度的模型预测控制系统进行比较。我们的研究结果在仿真和真实的21Kg Aliengo四足机器人上得到了验证，表明当机器人受到零或中等干扰时，我们的方法与传统模型预测控制策略不相上下；而在处理持续的外部干扰时，它超越了基于梯度的方法，这得益于在其公式化中能够实现的直接步态适应策略。
英文摘要：
Quadrupedal robots excel in mobility, navigating complex terrains with agility. However, their complex control systems present challenges that are still far from being fully addressed. In this paper, we introduce the use of Sample-Based Stochastic control strategies for quadrupedal robots, as an alternative to traditional optimal control laws. We show that Sample-Based Stochastic methods, supported by GPU acceleration, can be effectively applied to real quadruped robots. In particular, in this work, we focus on achieving gait frequency adaptation, a notable challenge in quadrupedal locomotion for gradient-based methods. To validate the effectiveness of Sample-Based Stochastic controllers we test two distinct approaches for quadrupedal robots and compare them against a conventional gradient-based Model Predictive Control system. Our findings, validated both in simulation and on a real 21Kg Aliengo quadruped, demonstrate that our method is on par with a traditional Model Predictive Control strategy when the robot is subject to zero or moderate disturbance, while it surpasses gradient-based methods in handling sustained external disturbances, thanks to the straightforward gait adaptation strategy that is possible to achieve within their formulation.
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