论文标题
使用生成模型来近似约束歧管，以基于采样的约束运动计划
Approximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planning
论文作者
论文摘要
在任务约束下基于采样的运动计划是具有挑战性的，因为配置空间中的无量限制歧管使拒绝抽样效率极低，即使不是不可能。本文提出了一种基于学习的抽样策略，以解决受限的运动计划问题。我们研究了两个众所周知的深层生成模型，即条件变分自动编码器（CVAE）和条件生成的对抗净（CGAN）来生成约束可满足的样品配置。我们使用以约束参数为条件的生成模型，而不是预算的图形，以近似约束歧管。这种方法允许在线绘制约束满足样本的有效绘制，而无需修改可用的基于采样的运动计划算法。我们根据其采样精度和采样分布的覆盖率评估了这两个生成模型的效率。还对两个机器人平台上的不同约束任务进行了模拟和实验。
Sampling-based motion planning under task constraints is challenging because the null-measure constraint manifold in the configuration space makes rejection sampling extremely inefficient, if not impossible. This paper presents a learning-based sampling strategy for constrained motion planning problems. We investigate the use of two well-known deep generative models, the Conditional Variational Autoencoder (CVAE) and the Conditional Generative Adversarial Net (CGAN), to generate constraint-satisfying sample configurations. Instead of precomputed graphs, we use generative models conditioned on constraint parameters for approximating the constraint manifold. This approach allows for the efficient drawing of constraint-satisfying samples online without any need for modification of available sampling-based motion planning algorithms. We evaluate the efficiency of these two generative models in terms of their sampling accuracy and coverage of sampling distribution. Simulations and experiments are also conducted for different constraint tasks on two robotic platforms.
