学習初期化を用いた動的脚式移動のためのリcedingホライズン知覚軌道最適化
Receding-Horizon Perceptive Trajectory Optimization for Dynamic Legged Locomotion with Learned Initialization
脚式ロボットが障害物を動的に越えるため、知覚と軌道最適化をオンラインで繰り返すパイプラインを提案。初期解をヒューリスティックまたは潜在モード軌道回帰で生成し、ANYmal Cで実機検証した。
著者: Oliwier Melon, Romeo Orsolino, David Surovik, Mathieu Geisert, Ioannis Havoutis, Maurice Fallon
分類: cs.RO
原文アブストラクト
To dynamically traverse challenging terrain, legged robots need to continually perceive and reason about upcoming features, adjust the locations and timings of future footfalls and leverage momentum strategically. We present a pipeline that enables flexibly-parametrized trajectories for perceptive and dynamic quadruped locomotion to be optimized in an online, receding-horizon manner. The initial guess passed to the optimizer affects the computation needed to achieve convergence and the quality of the solution. We consider two methods for generating good guesses. The first is a heuristic initializer which provides a simple guess and requires significant optimization but is nonetheless suitable for adaptation to upcoming terrain. We demonstrate experiments using the ANYmal C quadruped, with fully onboard sensing and computation, to cross obstacles at moderate speeds using this technique. Our second approach uses latent-mode trajectory regression (LMTR) to imitate expert data - while avoiding invalid interpolations between distinct behaviors - such that minimal optimization is needed. This enables high-speed motions that make more expansive use of the robot's capabilities. We demonstrate it on flat ground with the real robot and provide numerical trials that progress toward deployment on terrain. These results illustrate a paradigm for advancing beyond short-horizon dynamic reactions, toward the type of intuitive and adaptive locomotion planning exhibited by animals and humans.
