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.
翻译:在高维机器人系统中,有效配置空间的方块往往具有复杂的形状,特别是在终端效应定向或静态稳定性等制约下。我们提议一种基因对抗网络方法,以学习在这种制约下有效机器人配置的分布。它可以产生接近制约方块的配置。我们展示了这种方法的两个应用。首先,通过学习与理想终端效应位置有关的有条件分布,我们可以做快速反动运动学,即使是非常高的自由度系统。然后,我们用它来生成基于抽样的有限动作规划算法样本,以减少必要的投影步骤,加速计算。我们用7-DoF Panda操纵器和28-DoF人形机器人图洛斯来验证模拟方法。