C-Free-Uniform: A Map-Conditioned Trajectory Sampler for Model Predictive Path Integral Control
Abstract
Trajectory sampling is a key component of sampling-based control mechanisms. Trajectory samplers rely on control input samplers, which generate control inputs u from a distribution p(u | x) where x is the current state. We introduce the notion of Free Configuration Space Uniformity (C-Free-Uniform for short) which has two key features: (i) it generates a control input distribution so as to uniformly sample the free configuration space, and (ii) in contrast to previously introduced trajectory sampling mechanisms where the distribution p(u | x) is independent of the environment, C-Free-Uniform is explicitly conditioned on the current local map. Next, we integrate this sampler into a new Model Predictive Path Integral (MPPI) Controller, CFU-MPPI. Experiments show that CFU-MPPI outperforms existing methods in terms of success rate in challenging navigation tasks in cluttered polygonal environments while requiring a much smaller sampling budget.
Cite
@article{arxiv.2510.16905,
title = {C-Free-Uniform: A Map-Conditioned Trajectory Sampler for Model Predictive Path Integral Control},
author = {Yukang Cao and Rahul Moorthy and O. Goktug Poyrazoglu and Volkan Isler},
journal= {arXiv preprint arXiv:2510.16905},
year = {2025}
}
Comments
Submitted to the 2026 IEEE International Conference on Robotics and Automation (ICRA). 8 pages, 4 figures