GANで学ぶロボット関節配置の制約付き分布
Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network
GANを用いて制約下での有効なロボット関節配置の分布を学習し、高自由度系の高速逆運動学や制約付き動作計画のサンプリング効率化を実現した。
著者: Teguh Santoso Lembono, Emmanuel Pignat, Julius Jankowski, Sylvain Calinon
分類: cs.RO, cs.LG
原文アブストラクト
In high dimensional robotic system, the manifold of the valid configuration space often has a complex shape, especially under constraints such as end-effector orientation or static stability. We propose a generative adversarial network approach to learn the distribution of valid robot configurations under such constraints. It can generate configurations that are close to the constraint manifold. We present two applications of this method. First, by learning the conditional distribution with respect to the desired end-effector position, we can do fast inverse kinematics even for very high degrees of freedom (DoF) systems. Then, we use it to generate samples in sampling-based constrained motion planning algorithms to reduce the necessary projection steps, speeding up the computation. We validate the approach in simulation using the 7-DoF Panda manipulator and the 28-DoF humanoid robot Talos.
