剛体機構の逆運動学に対する遠位教師学習と数値・解析手法の比較
Comparison of Distal Teacher Learning with Numerical and Analytical Methods to Solve Inverse Kinematics for Rigid-Body Mechanisms
剛体機構の逆運動学を解くため、機械学習手法である遠位教師学習(DT)を微分可能プログラミングと組み合わせ、解析解・数値解と解率・精度・サンプル効率・スケーラビリティ・実行時間などで広く比較評価した。
著者: Tim von Oehsen, Alexander Fabisch, Shivesh Kumar, Frank Kirchner
分類: cs.RO, cs.LG
原文アブストラクト
Several publications are concerned with learning inverse kinematics, however, their evaluation is often limited and none of the proposed methods is of practical relevance for rigid-body kinematics with a known forward model. We argue that for rigid-body kinematics one of the first proposed machine learning (ML) solutions to inverse kinematics -- distal teaching (DT) -- is actually good enough when combined with differentiable programming libraries and we provide an extensive evaluation and comparison to analytical and numerical solutions. In particular, we analyze solve rate, accuracy, sample efficiency and scalability. Further, we study how DT handles joint limits, singularities, unreachable poses, trajectories and provide a comparison of execution times. The three approaches are evaluated on three different rigid body mechanisms with varying complexity. With enough training data and relaxed precision requirements, DT has a better solve rate and is faster than state-of-the-art numerical solvers for a 15-DoF mechanism. DT is not affected by singularities while numerical solutions are vulnerable to them. In all other cases numerical solutions are usually better. Analytical solutions outperform the other approaches by far if they are available.
