ロボティクス向け射影ベース一次制約付き最適化ソルバ
Projection-based first-order constrained optimization solver for robotics
ユークリッド射影で幾何制約を活用する一次法ALSPGを提案し、IKやMPCで二次法に匹敵する性能を示した。
著者: Hakan Girgin, Tobias Löw, Teng Xue, Sylvain Calinon
分類: cs.RO
原文アブストラクト
Robot programming tools ranging from inverse kinematics (IK) to model predictive control (MPC) are most often described as constrained optimization problems. Even though there are currently many commercially-available second-order solvers, robotics literature recently focused on efficient implementations and improvements over these solvers for real-time robotic applications. However, most often, these implementations stay problem-specific and are not easy to access or implement, or do not exploit the geometric aspect of the robotics problems. In this work, we propose to solve these problems using a fast, easy-to-implement first-order method that fully exploits the geometric constraints via Euclidean projections, called Augmented Lagrangian Spectral Projected Gradient Descent (ALSPG). We show that 1. using projections instead of full constraints and gradients improves the performance of the solver and 2. ALSPG stays competitive to the standard second-order methods such as iLQR in the unconstrained case. We showcase these results with IK and motion planning problems on simulated examples and with an MPC problem on a 7-axis manipulator experiment.
