用图注意力机制提升机器人运动规划的采样效率。
GAIDE: Graph-based Attention Masking for Spatial- and Embodiment-aware Motion Planning
- 将空间结构与机械臂形态建模为图，通过注意力掩码指导采样。
- 在高维空间中实现更高成功率与更快规划速度。
- 适合需要高效运动规划的机器人系统开发者。
基于采样的运动规划算法广泛用于机器人机械臂运动规划，但在高维配置空间中常因依赖均匀或手工设计的采样策略而面临采样效率低下问题。神经网络启发式采样器通过学习过往规划经验中的采样分布来引导规划器向目标靠近。然而，现有方法往往难以捕捉运动规划问题中的空间结构。为此，我们提出图注意力掩码的时空与具身感知运动规划方法（GAIDE），一种基于Transformer的神经启发式采样器，利用任务空间结构和机械臂本体特征构建图结构，并通过注意力掩码融入采样过程。我们在统一采样、手工启发式采样及神经启发式采样基线上评估了GAIDE，结果表明其显著提升了规划效率与成功率。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。