IKFlow：多様な逆運動学解を生成する手法
IKFlow: Generating Diverse Inverse Kinematics Solutions
深層ニューラルネットワークを用いて、6自由度以上のロボットアームの逆運動学解を多様に生成する手法を提案。高速かつ高精度で、古典的手法による微調整も可能。
著者: Barrett Ames, Jeremy Morgan, George Konidaris
分類: cs.RO
原文アブストラクト
Inverse kinematics - finding joint poses that reach a given Cartesian-space end-effector pose - is a common operation in robotics, since goals and waypoints are typically defined in Cartesian space, but robots must be controlled in joint space. However, existing inverse kinematics solvers return a single solution pose, where systems with more than 6 degrees of freedom support infinitely many such solutions, which can be useful in the presence of constraints, pose preferences, or obstacles. We introduce a method that uses a deep neural network to learn to generate a diverse set of samples from the solution space of such kinematic chains. The resulting samples can be generated quickly (2000 solutions in under 10ms) and accurately (to within 10 millimeters and 2 degrees of an exact solution) and can be rapidly refined by classical methods if necessary.
