# GP-guided MPPI for Efficient Navigation in Complex Unknown Cluttered Environments

arXiv: https://arxiv.org/abs/2307.04019 · HTML: https://mlsanity.com/paper/2307.04019/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 8 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [GPflow: A Gaussian process library using TensorFlow](https://mlsanity.com/paper/1610.08733/) (2016)
- [Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control](https://mlsanity.com/paper/1811.01848/) (2018)
- [Where to go next: Learning a Subgoal Recommendation Policy for Navigation Among Pedestrians](https://mlsanity.com/paper/2102.13073/) (2021)
- [Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning](https://mlsanity.com/paper/2203.02381/) (2022)
- [Autonomous Navigation of AGVs in Unknown Cluttered Environments: log-MPPI Control Strategy](https://mlsanity.com/paper/2203.16599/) (2022)
- [Trajectory Distribution Control for Model Predictive Path Integral Control using Covariance Steering](https://mlsanity.com/paper/2109.12147/) (2021)
- [Model Predictive Path Integral Control Framework for Partially Observable Navigation: A Quadrotor Case Study](https://mlsanity.com/paper/2004.08641/) (2020)
- [MPPI-VS: Sampling-Based Model Predictive Control Strategy for Constrained Image-Based and Position-Based Visual Servoing](https://mlsanity.com/paper/2104.04925/) (2021)
- [Light-Weight Pointcloud Representation with Sparse Gaussian Process](https://mlsanity.com/paper/2301.11251/) (2023)
- [RRT Guided Model Predictive Path Integral Method](https://mlsanity.com/paper/2301.13143/) (2023)

## Abstract (arXiv)

Robotic navigation in unknown, cluttered environments with limited sensing capabilities poses significant challenges in robotics. Local trajectory optimization methods, such as Model Predictive Path Intergal (MPPI), are a promising solution to this challenge. However, global guidance is required to ensure effective navigation, especially when encountering challenging environmental conditions or navigating beyond the planning horizon. This study presents the GP-MPPI, an online learning-based control strategy that integrates MPPI with a local perception model based on Sparse Gaussian Process (SGP). The key idea is to leverage the learning capability of SGP to construct a variance (uncertainty) surface, which enables the robot to learn about the navigable space surrounding it, identify a set of suggested subgoals, and ultimately recommend the optimal subgoal that minimizes a predefined cost function to the local MPPI planner. Afterward, MPPI computes the optimal control sequence that satisfies the robot and collision avoidance constraints. Such an approach eliminates the necessity of a global map of the environment or an offline training process. We validate the efficiency and robustness of our proposed control strategy through both simulated and real-world experiments of 2D autonomous navigation tasks in complex unknown environments, demonstrating its superiority in guiding the robot safely towards its desired goal while avoiding obstacles and escaping entrapment in local minima. The GPU implementation of GP-MPPI, including the supplementary video, is available at https://github.com/IhabMohamed/GP-MPPI.