学習初期化と解析コストによる動的四足歩行の信頼性向上
Reliable Trajectories for Dynamic Quadrupeds using Analytical Costs and Learned Initializations
軌道最適化フレームワークTOWRに解析的コストとオフライン学習による初期化を追加し、実機四足ロボットで16歩・5.5秒の動的軌道を異なる地形で実行できるようにした。
著者: Oliwier Melon, Mathieu Geisert, David Surovik, Ioannis Havoutis, Maurice Fallon
分類: cs.RO
原文アブストラクト
Dynamic traversal of uneven terrain is a major objective in the field of legged robotics. The most recent model predictive control approaches for these systems can generate robust dynamic motion of short duration; however, planning over a longer time horizon may be necessary when navigating complex terrain. A recently-developed framework, Trajectory Optimization for Walking Robots (TOWR), computes such plans but does not guarantee their reliability on real platforms, under uncertainty and perturbations. We extend TOWR with analytical costs to generate trajectories that a state-of-the-art whole-body tracking controller can successfully execute. To reduce online computation time, we implement a learning-based scheme for initialization of the nonlinear program based on offline experience. The execution of trajectories as long as 16 footsteps and 5.5 s over different terrains by a real quadruped demonstrates the effectiveness of the approach on hardware. This work builds toward an online system which can efficiently and robustly replan dynamic trajectories.
