基于图的注意力掩码的时空和具身感知运动规划
GAIDE: Graph-based Attention Masking for Spatial- and Embodiment-aware Motion Planning
AI总结：
GAIDE是一种基于图的神经指导采样器，通过结合空间结构和机械臂的具身性提升运动规划的效率和成功率。
摘要：
基于采样的运动规划算法广泛用于机械臂的运动规划，但它们在高维配置空间中由于依赖于均匀或手工设计的指导采样原语而常常面临采样效率低下的问题。神经指导采样器通过从先前规划经验中学习采样分布来解决这一限制，以引导运动规划器朝着规划目标前进。然而，现有方法往往难以编码运动规划问题中固有的空间结构。为了解决这一限制，我们引入了基于图的注意力掩码的时空和具身感知运动规划（GAIDE），这是一种神经指导采样器，利用规划问题中的空间结构和机械臂的具身性来引导规划算法。GAIDE将这些结构表示为图，并通过注意力掩码将其整合到基于变换器的神经采样器中。我们使用均匀采样、手工设计的指导采样和神经指导采样原语评估GAIDE与基线的最先进基于采样的规划器。评估结果表明，GAIDE提高了规划效率和成功率。
英文摘要：
Sampling-based motion planning algorithms are widely used for motion planning of robotic manipulators, but they often struggle with sample inefficiency in high-dimensional configuration spaces due to their reliance on uniform or hand-crafted informed sampling primitives. Neural informed samplers address this limitation by learning the sampling distribution from prior planning experience to guide the motion planner towards planning goal. However, existing approaches often struggle to encode the spatial structure inherent in motion planning problems. To address this limitation, we introduce Graph-based Attention Masking for Spatial- and Embodiment-aware Motion Planning (GAIDE), a neural informed sampler that leverages both the spatial structure of the planning problem and the robotic manipulator's embodiment to guide the planning algorithm. GAIDE represents these structures as a graph and integrates it into a transformer-based neural sampler through attention masking. We evaluate GAIDE against baseline state-of-the-art sampling-based planners using uniform sampling, hand-crafted informed sampling, and neural informed sampling primitives. Evaluation results demonstrate that GAIDE improves planning efficiency and success rate.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
