軌道生成のためのシンボリック最適化ファブリックの自動チューニング
Autotuning Symbolic Optimization Fabrics for Trajectory Generation
軌道生成手法のパラメータをベイズ最適化で自動調整する枠組みを提案し、シンボリックファブリックに適用して数回の試行で専門家並みの性能を達成、ロボット間やシミュレーション・実機間での転移も示した。
著者: Max Spahn, Javier Alonso-Mora
分類: cs.RO
原文アブストラクト
In this paper, we present an automated parameter optimization method for trajectory generation. We formulate parameter optimization as a constrained optimization problem that can be effectively solved using Bayesian optimization. While the approach is generic to any trajectory generation method, we showcase it using optimization fabrics. Optimization fabrics are a geometric trajectory generation method based on non-Riemannian geometry. By symbolically pre-solving the structure of the tree of fabrics, we obtain a parameterized trajectory generator, called symbolic fabrics. We show that autotuned symbolic fabrics reach expert-level performance in a few trials. Additionally, we show that tuning transfers across different robots, motion planning problems and between simulation and real world. Finally, we qualitatively showcase that the framework could be used for coupled mobile manipulation.
