论文标题
学习和使用抽象进行机器人计划
Learning and Using Abstractions for Robot Planning
论文作者
论文摘要
机器人运动计划涉及计算一系列有效的机器人配置，这些序列将机器人从其初始状态带到目标状态。使用分析方法最佳地解决运动计划问题已被证明是pspace-hard。基于抽样的方法试图有效地近似最佳解决方案。通常，基于抽样的计划者使用统一的采样器来覆盖整个状态空间。在本文中，我们提出了一个基于学习的框架，该框架标识了环境中的机器人配置，这对于解决给定的运动计划问题很重要。这些状态用于偏向抽样分布，以减少计划时间。我们的方法与统一网络一起使用，并根据环境和机器人生成依赖域的网络参数。我们在三种不同的设置中通过学习和链接计划者评估我们的方法。与当前基于抽样的运动计划者相比，结果显示运动计划时间的显着改善。
Robot motion planning involves computing a sequence of valid robot configurations that take the robot from its initial state to a goal state. Solving a motion planning problem optimally using analytical methods is proven to be PSPACE-Hard. Sampling-based approaches have tried to approximate the optimal solution efficiently. Generally, sampling-based planners use uniform samplers to cover the entire state space. In this paper, we propose a deep-learning-based framework that identifies robot configurations in the environment that are important to solve the given motion planning problem. These states are used to bias the sampling distribution in order to reduce the planning time. Our approach works with a unified network and generates domain-dependent network parameters based on the environment and the robot. We evaluate our approach with Learn and Link planner in three different settings. Results show significant improvement in motion planning times when compared with current sampling-based motion planners.
