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.

翻译：四足机器人凭借其敏捷性在复杂地形中展现了卓越的移动能力。然而，其复杂的控制系统仍面临诸多未完全解决的挑战。本文提出将基于样本的随机控制策略作为传统最优控制律的替代方案，应用于四足机器人。研究表明，借助GPU加速的样本随机方法可有效部署于真实四足机器人。具体而言，本研究聚焦于步态频率自适应这一梯度方法在四足运动中的显著难题。为验证样本随机控制器的有效性，我们测试了两类四足机器人控制方法，并将其与传统基于梯度的模型预测控制系统进行对比。在仿真环境及21公斤级真实Aliengo四足机器人上开展的验证表明：当机器人受零或中等干扰时，本方法性能与传统模型预测控制策略相当；而在持续外部干扰场景下，由于可在算法框架内实现直接的步态自适应策略，本方法在鲁棒性上超越梯度方法。