Motion planning framed as optimisation in structured latent spaces has recently emerged as competitive with traditional methods in terms of planning success while significantly outperforming them in terms of computational speed. However, the real-world applicability of recent work in this domain remains limited by the need to express obstacle information directly in state-space, involving simple geometric primitives. In this work we address this challenge by leveraging learned scene embeddings together with a generative model of the robot manipulator to drive the optimisation process. In addition, we introduce an approach for efficient collision checking which directly regularises the optimisation undertaken for planning. Using simulated as well as real-world experiments, we demonstrate that our approach, AMP-LS, is able to successfully plan in novel, complex scenes while outperforming traditional planning baselines in terms of computation speed by an order of magnitude. We show that the resulting system is fast enough to enable closed-loop planning in real-world dynamic scenes.

翻译：运动规划问题在结构化潜在空间中以优化形式提出，近年来在规划成功率方面与传统方法不相上下，而在计算速度上则显著超越后者。然而，该领域近期工作的实际应用仍受限于需在状态空间中直接表示障碍物信息（通常涉及简单几何基元）。针对这一挑战，本文通过学习场景嵌入，结合机器人操作臂的生成模型来驱动优化过程。此外，我们引入了一种高效碰撞检测方法，可直接约束规划中的优化迭代。通过仿真与真实世界实验，我们证明所提出的AMP-LS方法能在新颖复杂场景中成功规划，同时计算速度比传统规划基线提升一个数量级。我们进一步展示，该系统的实时性足以支持真实世界动态场景中的闭环规划。