SIL-RRT*: Learning Sampling Distribution through Self Imitation Learning
Robotics
2024-11-27 v1
Abstract
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.
Cite
@article{arxiv.2411.17293,
title = {SIL-RRT*: Learning Sampling Distribution through Self Imitation Learning},
author = {Xuzhe Dang and Stefan Edelkamp},
journal= {arXiv preprint arXiv:2411.17293},
year = {2024}
}