潜在空間制御による四足歩行のセマンティック制約付き最適化
First Steps: Latent-Space Control with Semantic Constraints for Quadruped Locomotion
四足歩行制御を構造化潜在空間での最適化として定式化し、深層生成モデルと学習済み分類器で複雑な制約を微分可能かつ高速に扱う手法を提案、実機ANYmalで検証した。
著者: Alexander L. Mitchell, Martin Engelcke, Oiwi Parker Jones, David Surovik, Siddhant Gangapurwala, Oliwier Melon, Ioannis Havoutis, Ingmar Posner
分類: cs.RO, cs.LG
原文アブストラクト
Traditional approaches to quadruped control frequently employ simplified, hand-derived models. This significantly reduces the capability of the robot since its effective kinematic range is curtailed. In addition, kinodynamic constraints are often non-differentiable and difficult to implement in an optimisation approach. In this work, these challenges are addressed by framing quadruped control as optimisation in a structured latent space. A deep generative model captures a statistical representation of feasible joint configurations, whilst complex dynamic and terminal constraints are expressed via high-level, semantic indicators and represented by learned classifiers operating upon the latent space. As a consequence, complex constraints are rendered differentiable and evaluated an order of magnitude faster than analytical approaches. We validate the feasibility of locomotion trajectories optimised using our approach both in simulation and on a real-world ANYmal quadruped. Our results demonstrate that this approach is capable of generating smooth and realisable trajectories. To the best of our knowledge, this is the first time latent space control has been successfully applied to a complex, real robot platform.
