LOInK: 構造化ニューラルサロゲートモデルによる学習型最適逆運動学
LOInK: Learned Optimal Inverse Kinematics via Structured Neural Surrogate Models
コスト最小の逆運動学解を効率的に生成するため、構成空間をタスク・潜在空間へ双リプシッツ可逆写像で学習し、演算子分割によるネットワーク反転で解をサンプリングする手法を提案。
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著者: Michael Somerfield, Damian Abood, Ruigang Wang, Ian R. Manchester
分類: cs.RO
原文アブストラクト
We introduce Learned Optimal Inverse Kinematics (LOInK), a method to generate approximately optimal solutions to an inverse kinematics problem. When trained on data consisting of sampled configurations and associated task variables and a given cost function, LOInK learns a bi-Lipschitz invertible mapping from configuration space to a decoupled task/latent space, and moreover, the latent space is structured so as to place cost-minimizing solutions at the origin. This enables efficient sampling of cost-minimizing solutions via a network-inversion algorithm based on operator splitting. We demonstrate the proposed approach on three problems: an illustrative three degree-of-freedom manipulator problem; a quadrupedal climbing robot for which LOInK can generate near-optimal solutions on average 31 times faster and up to 100 times faster than a constrained optimization approach; and a simulated soft actuator as a purely data-driven example, in which LOInK can explicitly generate high-quality solutions, unlike existing generative approaches that require diverse sampling and evaluation of candidate solutions.