用自模仿学习优化采样分布，提升机器人路径规划效率。
SIL-RRT*: Learning Sampling Distribution through Self Imitation Learning
- 用神经网络预测采样分布，替代传统随机采样。
- 在2D/3D环境中以更少样本完成高维路径规划。
- 适合复杂环境下的机器人运动规划任务。
高效生成移动物体的安全可行轨迹是机器人学与计算机科学的关键挑战。本文提出SIL-RRT*，一种基于学习的运动规划算法，通过深度神经网络在每轮迭代中预测采样分布，扩展了RRT*算法。我们在多种二维和三维环境中评估该方法，结果表明，SIL-RRT*能以比传统采样算法更少的样本解决高维运动规划问题，并可有效扩展至更复杂的环境，展现出在解决复杂机器人运动规划问题上的巨大潜力。
原文摘要 · Abstract (English)
Efficiently finding safe and feasible trajectories for mobile objects is a critical field in robotics and computer science. In this paper, we propose SIL-RRT*, a novel learning-based motion planning algorithm that extends the RRT* algorithm by using a deep neural network to predict a distribution for sampling at each iteration. We evaluate SIL-RRT* on various 2D and 3D environments and establish that it can efficiently solve high-dimensional motion planning problems with fewer samples than traditional sampling-based algorithms. Moreover, SIL-RRT* is able to scale to more complex environments, making it a promising approach for solving challenging robotic motion planning problems.
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