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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.

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// repository documentation

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# 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](https://raw.githubusercontent.com/leggedrobotics/foci/main/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.

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