论文标题
扩展摘要：运动计划者从几何幻觉中学到的
Extended Abstract: Motion Planners Learned from Geometric Hallucination
论文作者
论文摘要
学习运动计划者以无碰撞的方式将机器人从障碍物占领的空间中移至另一点需要大量数据或高质量的演示。这一要求是由于机器人可以执行的各种操作中的事实引起的，如果没有许多试用和错误或已经有能力这样做的专家，就很难找到单个最佳计划。但是，鉴于在无障碍空间中执行的计划，找到该计划最佳的障碍几何形状相对容易。我们认为，经典运动计划的“双重”问题，并将发现适当的障碍几何形状作为幻觉的过程命名。在这项工作中，我们提出了两种不同的幻觉方法（1）最受限制的和（2）最小的障碍空间，在探索阶段，在完全安全的无障碍环境中执行的给定计划仍然是最佳的。然后，我们训练一个端到端的运动计划者，该计划者可以产生动议，以在部署过程中移动现实的障碍。两种方法均在现实世界中混乱环境中的物理移动机器人上进行测试。
Learning motion planners to move robot from one point to another within an obstacle-occupied space in a collision-free manner requires either an extensive amount of data or high-quality demonstrations. This requirement is caused by the fact that among the variety of maneuvers the robot can perform, it is difficult to find the single optimal plan without many trial-and-error or an expert who is already capable of doing so. However, given a plan performed in obstacle-free space, it is relatively easy to find an obstacle geometry, where this plan is optimal. We consider this "dual" problem of classical motion planning and name this process of finding appropriate obstacle geometry as hallucination. In this work, we present two different approaches to hallucinate (1) the most constrained and (2) a minimal obstacle space where a given plan executed during an exploration phase in a completely safe obstacle-free environment remains optimal. We then train an end-to-end motion planner that can produce motions to move through realistic obstacles during deployment. Both methods are tested on a physical mobile robot in real-world cluttered environments.
