On the Benefits of GPU Sample-Based Stochastic Predictive Controllers for Legged Locomotion
arxiv(2024)
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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