# MLNav: Learning to Safely Navigate on Martian Terrains

arXiv: https://arxiv.org/abs/2203.04563 · HTML: https://mlsanity.com/paper/2203.04563/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 20 of 20)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [Learning Sampling Distributions for Robot Motion Planning](https://mlsanity.com/paper/1709.05448/) (2017)
- [PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning](https://mlsanity.com/paper/1710.03937/) (2017)
- [Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners](https://mlsanity.com/paper/1907.06013/) (2019)
- [From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robots](https://mlsanity.com/paper/1609.07910/) (2016)
- [Data-driven Planning via Imitation Learning](https://mlsanity.com/paper/1711.06391/) (2017)
- [Verifiably Safe Exploration for End-to-End Reinforcement Learning](https://mlsanity.com/paper/2007.01223/) (2020)
- [Watch This: Scalable Cost-Function Learning for Path Planning in Urban Environments](https://mlsanity.com/paper/1607.02329/) (2016)
- [Intention-Net: Integrating Planning and Deep Learning for Goal-Directed Autonomous Navigation](https://mlsanity.com/paper/1710.05627/) (2017)
- [Learning Transferable Policies for Monocular Reactive MAV Control](https://mlsanity.com/paper/1608.00627/) (2016)
- [Learning-Based Proxy Collision Detection for Robot Motion Planning Applications](https://mlsanity.com/paper/1902.08164/) (2019)
- [A Critical Investigation of Deep Reinforcement Learning for Navigation](https://mlsanity.com/paper/1802.02274/) (2018)
- [Fast Approximate Clearance Evaluation for Rovers with Articulated Suspension Systems](https://mlsanity.com/paper/1808.00031/) (2018)
- [Following Social Groups: Socially Compliant Autonomous Navigation in Dense Crowds](https://mlsanity.com/paper/1911.12063/) (2019)
- [Neural Tree Expansion for Multi-Robot Planning in Non-Cooperative Environments](https://mlsanity.com/paper/2104.09705/) (2021)
- [Machine Learning Based Path Planning for Improved Rover Navigation (Pre-Print Version)](https://mlsanity.com/paper/2011.06022/) (2020)
- [Neural Collision Clearance Estimator for Batched Motion Planning](https://mlsanity.com/paper/1910.05917/) (2019)
- [Vision and Learning for Deliberative Monocular Cluttered Flight](https://mlsanity.com/paper/1411.6326/) (2014)
- [Learning to Navigate: Exploiting Deep Networks to Inform Sample-Based Planning During Vision-Based Navigation](https://mlsanity.com/paper/1801.05132/) (2018)

## Built on by (most cited first, 3 of 3)

- [Mars Traversability Prediction: A Multi-modal Self-supervised Approach for Costmap Generation](https://mlsanity.com/paper/2509.11082/) (2025)
- [MarsRetrieval: Benchmarking Vision-Language Models for Planetary-Scale Geospatial Retrieval on Mars](https://mlsanity.com/paper/2602.13961/) (2026)
- [Neural Radiance Maps for Extraterrestrial Navigation and Path Planning](https://mlsanity.com/paper/2603.17236/) (2026)

## Abstract (arXiv)

We present MLNav, a learning-enhanced path planning framework for safety-critical and resource-limited systems operating in complex environments, such as rovers navigating on Mars. MLNav makes judicious use of machine learning to enhance the efficiency of path planning while fully respecting safety constraints. In particular, the dominant computational cost in such safety-critical settings is running a model-based safety checker on the proposed paths. Our learned search heuristic can simultaneously predict the feasibility for all path options in a single run, and the model-based safety checker is only invoked on the top-scoring paths. We validate in high-fidelity simulations using both real Martian terrain data collected by the Perseverance rover, as well as a suite of challenging synthetic terrains. Our experiments show that: (i) compared to the baseline ENav path planner on board the Perserverance rover, MLNav can provide a significant improvement in multiple key metrics, such as a 10x reduction in collision checks when navigating real Martian terrains, despite being trained with synthetic terrains; and (ii) MLNav can successfully navigate highly challenging terrains where the baseline ENav fails to find a feasible path before timing out.