LEAP: 接触拘束ロボットのための勾配流拡張ラグランジアン軌道最適化
LEAP: Gradient Flow Augmented Lagrangian Trajectory Optimization for Contact Constrained Robots
接触力を摩擦円錐制約下で扱う軌道最適化を、拡張ラグランジアンの主双対勾配流で解く手法LEAPを提案し、脚式・マニピュレーションロボットで高速かつ高信頼に解けることを示した。
著者: César E. Ramos Chuquiure, Ryan Tsai, Yutong Zhou, Zihang Huang, Vansh Thakur, Akshunn Trivedi, Zachary Lin, Pearl Lin, Ruikai Yang, Challen Enninful Adu, Ram Vasudevan
分類: cs.RO
原文アブストラクト
Generating dynamically feasible motions for legged and manipulating robots requires solving a large, nonlinear, non-convex optimal control problem (OCP) in which contact wrenches must satisfy friction-cone constraints throughout the motion, not only at isolated instants. Existing solvers are sensitive to the initial guess and scale poorly with the number of joints. This paper introduces LEAP, which transcribes the OCP with pseudospectral collocation and, instead of a Newton or SQP solve, integrates a primal--dual gradient flow of a Powell--Hestenes--Rockafellar augmented Lagrangian to compute a solution. Because the flow is stiff, this paper designs a linearly implicit integrator with a Gauss--Newton metric, momentum, and a merit-based step controller that keeps the augmented Lagrangian monotone. Across locomotion benchmarks on five robot platforms, from planar hoppers to 32-joint humanoids, LEAP is the only method to solve every tested problem, achieves substantial speedups over Crocoddyl and Aligator on several benchmarks, and reaches trackable references at smaller compute budgets. The resulting trajectories are validated by tracking them in a high-fidelity simulator and on hardware. We will make the code public upon final acceptance of this paper.