https://github.com/leggedrobotics/foci

Fast, orientation-aware trajectory planning using a novel Gaussian overlap-based collision formulation, modeling both robot and environment as Gaussian Splat.
https://github.com/leggedrobotics/foci

gaussian-splatting orientation-awareness trajectory-optimization

Last synced: 11 months ago
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Fast, orientation-aware trajectory planning using a novel Gaussian overlap-based collision formulation, modeling both robot and environment as Gaussian Splat.

• Host: GitHub
• URL: https://github.com/leggedrobotics/foci
• Owner: leggedrobotics
• License: mit
• Created: 2025-05-14T06:52:48.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-07-16T10:46:10.000Z (12 months ago)
• Last Synced: 2025-07-17T14:38:44.912Z (11 months ago)
• Topics: gaussian-splatting, orientation-awareness, trajectory-optimization
• Language: Python
• Homepage: https://rffr.leggedrobotics.com/works/foci/
• Size: 47.9 MB
• Stars: 10
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# FOCI: Trajectory Optimization on Gaussian Splats  

- **Project page:** [https://rffr.leggedrobotics.com/works/foci/](https://rffr.leggedrobotics.com/works/foci/)

## Authors

- Mario Gomez Andreu\*¹

- Maximum Wilder-Smith\*¹

- Victor Klemm¹

- Vaishakh Patil¹

- Jesus Tordesillas²

- Marco Hutter¹

\*Equal contribution  

¹ Robotic Systems Lab, ETH Zurich  

² Comillas Pontifical University

## Overview

![Stonehenge paths](./docs/stonerobot.gif)

FOCI is a novel method to compute orientation aware trajectories for robots using 3D Gaussian Splats to model both the robot and the environment. 

## Installation

0. Install `git lfs`

1. Clone the repo with `git clone git@github.com:leggedrobotics/foci.git`

2. Obtain a licence for the `MA27` solver from [https://licences.stfc.ac.uk/product/coin-hsl](https://licences.stfc.ac.uk/product/coin-hsl), download the corresponding ZIP file, rename the extracted folder to `coinhsl` and move it to the root directory of this repository. 

3. `docker build -t rsl/foci .` to build the provided docker container.

4. `docker run -it -v .:/workspace --gpus all -p 127.0.0.1:8080:8080 rsl/foci` to run and attach to the container.

5. `pip install -e .` to install the `foci` in the docker container.

6. `python3 demos/stonehenge.py` to run the demo script. Open `127.0.0.1:8080` in your webbrowser to a see a visualisation similar to the one in this `README.md`

## Citing

If you find this work useful, please consider citing our paper:

```bibtex

@article{andreuwildersmith2025foci,

        author        = {Mario Gomez Andreu and Maximum Wilder-Smith and Victor Klemm and Vaishakh Patil and Jesus Tordesillas and Marco Hutter},

        title         = {FOCI: Trajectory Optimization on Gaussian Splats},

        year          = {2025},

        eprint        = {2505.08510},

        archivePrefix = {arXiv},

        primaryClass  = {cs.RO},

        url           = {https://arxiv.org/abs/2505.08510}

}

```

## Data Attribution

The Gaussian splat file (`demo/data/stonehenge.ply`) included in this repository was generated using processed data from the [Splat-Nav project](https://github.com/chengine/splatnav), which is licensed under the MIT License. 

We acknowledge the authors of Splat-Nav for making their resources available.