在潜在空间中利用学习的距离梯度进行局部路径优化
Local Path Optimization in The Latent Space Using Learned Distance Gradient
AI总结：
本文提出利用学习的距离梯度在潜在空间中进行局部路径优化，通过训练神经网络预测机器人与障碍物的距离，提高路径规划效率。
摘要：
约束运动规划是机械手操作中的常见但具有挑战性的问题。近年来，数据驱动的约束运动规划算法展示了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。由于流形近似中的误差以及在潜在空间中准确识别碰撞冲突的困难，耗时的路径有效性检查和路径重规划是必需的。在本文中，我们提出了一种方法，训练神经网络使用潜在向量作为输入来预测机器人与障碍物之间的最小距离。学习到的距离梯度随后用于计算潜在空间中的运动方向，以将机器人远离障碍物。基于此，提出了一种在潜在空间中的局部路径优化算法，并将其与路径有效性检查过程相结合，以减少重规划的时间。所提出的方法在多个规划场景中与最先进的算法进行比较，展示了最快的规划速度。
英文摘要：
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
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。
