通过凸集神经图加速混合离散-连续运动规划

中文摘要 AI 辅助

碰撞-free导航、接触丰富的操纵等运动规划问题可自然表述为将离散决策与连续轨迹耦合的优化问题，凸集图（Graphs of Convex Sets, GCS）框架为此类问题提供了实用解决方案，它将离散决策表示为图的节点，并在连接节点的边中编码连续轨迹。然而，由此产生的优化子问题对于在线重规划而言可能在计算上过于昂贵。在本研究中，我们提出一种基于学习的策略来缓解这一限制，具体而言，我们用图注意力网络（Graph Attention Network）的单次前向传播替代常规GCS所需的高成本凸松弛步骤，该网络会预测图中一组高概率候选路径，随后一个轻量级排序网络会按估计的轨迹成本对这些候选路径进行排序，按此顺序评估它们，我们会提前终止搜索，同时仍能恢复近最优的运动规划。我们在多种机器人任务中验证了该流程，包括3D四旋翼和7自由度机械臂的无碰撞运动规划，以及平面推操作的接触规划。在凸和非凸成本与约束设置下，我们的方法相较于常规GCS实现了最高达两个数量级的加速，同时保持了100%的成功率，代价是恢复的解决方案存在一定的次优性。代码实现和视频演示可在该httpsURL获取。

英文摘要

Motion planning problems such as collision-free navigation and contact-rich manipulation can be naturally formulated as optimization problems that couple discrete decisions with continuous trajectories. The Graphs of Convex Sets (GCS) framework offers a practical solution to these problems. It represents discrete decisions as nodes of a graph and encodes continuous trajectories in the edges connecting them. However, the resulting optimization subproblems can become computationally prohibitive for online replanning. In this work, we propose a learning-based strategy to mitigate this limitation. Specifically, we replace the costly convex relaxation step required by nominal GCS with a single forward pass through a Graph Attention Network that predicts a set of highly probable candidate paths through the graph. A lightweight ranking network then orders these candidates by their estimated trajectory cost. Evaluating them in this order, we terminate our search early while still recovering a near-optimal motion plan. We validate the resulting pipeline across diverse robotic tasks, including collision-free motion planning for a 3D quadrotor and a 7-DoF manipulator, and planning through contact for planar pushing. Across both convex and non-convex cost and constraint settings, our approach yields up to two orders of magnitude speedup over nominal GCS while maintaining a 100% success rate, at the cost of some suboptimality in the recovered solutions. Code implementations and video demonstrations can be found at https://neural-gcs.github.io/.

发表机构

• Northeastern University（东北大学）
• University of Maryland（马里兰大学）
• Amazon（亚马逊公司）

机构由 AI 辅助整理，请以论文原文为准。