# Neural Autonomous Navigation with Riemannian Motion Policy

arXiv: https://arxiv.org/abs/1904.01762 · HTML: https://mlsanity.com/paper/1904.01762/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2019.

## Built on by (most cited first, 6 of 6)

- [RMP2: A Structured Composable Policy Class for Robot Learning](https://mlsanity.com/paper/2103.05922/) (2021)
- [Autonomous Driving Small-Scale Cars: A Survey of Recent Development](https://mlsanity.com/paper/2404.06229/) (2024)
- [One-shot Visual Imitation via Attributed Waypoints and Demonstration Augmentation](https://mlsanity.com/paper/2302.04856/) (2023)
- [Hierarchically Integrated Models: Learning to Navigate from Heterogeneous Robots](https://mlsanity.com/paper/2106.13280/) (2021)
- [MIMONet: Multi-Input Multi-Output On-Device Deep Learning](https://mlsanity.com/paper/2307.11962/) (2023)
- [Multi-Abstractive Neural Controller: An Efficient Hierarchical Control Architecture for Interactive Driving](https://mlsanity.com/paper/2305.14797/) (2023)

## Abstract (arXiv)

End-to-end learning for autonomous navigation has received substantial attention recently as a promising method for reducing modeling error. However, its data complexity, especially around generalization to unseen environments, is high. We introduce a novel image-based autonomous navigation technique that leverages in policy structure using the Riemannian Motion Policy (RMP) framework for deep learning of vehicular control. We design a deep neural network to predict control point RMPs of the vehicle from visual images, from which the optimal control commands can be computed analytically. We show that our network trained in the Gibson environment can be used for indoor obstacle avoidance and navigation on a real RC car, and our RMP representation generalizes better to unseen environments than predicting local geometry or predicting control commands directly.