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:作为结构化潜在空间优化框架的动力规划最近随着传统方法在规划成功率方面的竞争力而出现,在计算速度方面大大优于传统方法。然而,由于需要直接在州空间表达障碍信息,涉及简单的几何原始元素,最近在这一领域的工作的实际适用性仍然受到限制。在这项工作中,我们通过利用所学的场景嵌入和机器人操纵机的基因化模型来应对这一挑战。此外,我们引入了高效碰撞检查方法,直接规范了为规划所做的优化。我们利用模拟和现实世界实验,证明我们的AMP-LS方法能够成功地在新的复杂场景中进行规划,同时用数量级的计算速度超过传统的规划基线。我们显示,所产生的系统足够快,能够在现实世界动态场上进行闭环规划。</s>