用学习到的梯度优化潜在空间路径，加速机器人避障规划。
Local Path Optimization in The Latent Space Using Learned Distance Gradient
- 通过神经网络预测潜空间中机器人与障碍物距离梯度。
- 利用梯度方向在潜空间局部优化路径，减少重规划次数。
- 适合需要快速避障的机器人实时运动规划场景。
约束运动规划是机器人操作中的常见但极具挑战性的问题。近年来，数据驱动的约束运动规划算法展现出出色的规划速度和成功率。其中基于流形近似的潜在运动方法是最高效的规划算法。然而，由于流形近似误差以及在潜在空间中难以准确识别碰撞冲突，仍需耗时的路径有效性检查和重规划。本文提出一种方法，训练神经网络以潜在向量为输入，预测机器人与障碍物间的最小距离。利用学习到的距离梯度计算潜在空间中的移动方向，使机器人远离障碍物。基于此，提出一种潜在空间内的局部路径优化算法，并将其集成到路径有效性检查流程中，显著减少重规划时间。该方法在多个规划场景中与先进算法对比，展现出最快的规划速度。
原文摘要 · Abstract (English)
Constrained motion planning is a common but challenging problem in robotic manipulation. In recent years, data-driven constrained motion planning algorithms have shown impressive planning speed and success rate. Among them, the latent motion method based on manifold approximation is the most efficient planning algorithm. Due to errors in manifold approximation and the difficulty in accurately identifying collision conflicts within the latent space, time-consuming path validity checks and path replanning are required. In this paper, we propose a method that trains a neural network to predict the minimum distance between the robot and obstacles using latent vectors as inputs. The learned distance gradient is then used to calculate the direction of movement in the latent space to move the robot away from obstacles. Based on this, a local path optimization algorithm in the latent space is proposed, and it is integrated with the path validity checking process to reduce the time of replanning. The proposed method is compared with state-of-the-art algorithms in multiple planning scenarios, demonstrating the fastest planning speed
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