Skip to content

Latest commit

 

History

History
189 lines (153 loc) · 8.32 KB

README.md

File metadata and controls

189 lines (153 loc) · 8.32 KB
Raw

Words2Contact: Identifying Support Contacts from Verbal Instructions Using Foundation Models

GitHub

Paper Dataset Website arXiv

Official implementation of the paper "Words2Contact: Identifying Support Contacts from Verbal Instructions Using Foundation Models" presented at IEEE-RAS Humanoids 2024.

This repository contains the implementation of the LLMs/VLMs part of the project. For the multi-contact whole-body controller, please visit this repo.

For more details, visit the paper website.

Table of Contents

Repository Structure

.
├── .ci/                       # Docker configurations
│   └── Dockerfile             # Dockerfile to build the project's container
├── config/                    # Configuration files for models
│   └── GroundingDINO_SwinT_OGC.py # GroundingDINO configuration
├── data/                      # Test data and outputs
│   ├── test.png               # Example input image
│   └── test_output.png        # Example output image
├── media/                     # Media assets
│   ├── ack.png                # Acknowledgment image
│   └── concept_figure_wide.png # Conceptual figure for the project
├── submodules/                # External submodules
│   └── CLIP_Surgery/          # CLIP Surgery code and resources
├── words2contact/             # Core project source code
│   ├── grammar/               # Grammars for constraining language models
│   │   ├── classifier.gbnf    # Grammar for classifying outputs
│   │   └── README.md          # Grammar module documentation
│   ├── prompts/               # Prompts for LLMs
│   │   └── prompts.json       # JSON file with pre-defined prompts
│   ├── geom_utils.py          # Utilities for geometric calculations
│   ├── math_pars.py           # Parsing mathematical expressions
│   ├── saygment.py            # Language-grounded segmentation
│   ├── words2contacts.py      # Core script for Words2Contact
│   └── yello.py               # Language-grounded object detection
├── main.py                    # Entry point for the project
├── launch.sh                  # Docker launch script
├── object_detection.py        # Object detection testing
├── object_segmentation.py     # Object segmentation testing
└── README.md                  # Documentation (this file)

Prerequisites

Before starting, ensure you have the following:

  • Docker
  • NVIDIA Container Toolkit (if using GPU (recommended))
  • An OpenAI API Key (if using GPT-based LLMs). You can obtain it from OpenAI.

Installation

For now only Docker is supported, conda and pip installations will be added soon.

  1. Clone the repository:

    git clone https://github.com/hucebot/words2contact.git
cd words2contact

  2. Build the Docker image:

    docker build -t words2contact -f .ci/Dockerfile .

Usage

Set Up

If you plan to use OpenAI's GPT-based LLMs, set your API key as an environment variable before launching the Docker container:

export OPENAI_KEY=<your_openai_api_key>

Launching the Docker Container

Run the following command to start the container:

bash launch.sh

This will create a models/ folder in the root of the project where models will be downloaded and stored.

Quick Start

To test Words2Contact with the provided example image:

python main.py --image_path data/test.png --prompt "Place your hand above the red bowl."

The output will be saved as data/test_output.png.

More examples coming soon!

Command-Line Options

usage: main.py [-h] [--image_path IMAGE_PATH] [--prompt PROMPT] [--use_gpt] [--yello_vlm YELLO_VLM] [--output_path OUTPUT_PATH] [--llm_path LLM_PATH] [--chat_template CHAT_TEMPLATE]

Run Words2Contact with an image and a text prompt.

options:
  -h, --help                    show this help message and exit
  --image_path IMAGE_PATH       Path to the input image file. Default: 'data/test.png'.
  --prompt PROMPT               Text prompt for Words2Contact. Default: 'Place your hand above the red bowl.'.
  --use_gpt                     Use OpenAI API for the LLM (requires `OPENAI_KEY`).
  --yello_vlm YELLO_VLM         Model to use for YELLO VLM. Default: 'GroundingDINO'.
  --output_path OUTPUT_PATH     Path to save the output image. Default: 'data/test_output.png'.
  --llm_path LLM_PATH           Path to the `.gguf` LLM model weights.
  --chat_template CHAT_TEMPLATE Chat template to use for local LLMs. Default: 'ChatML'.

Using Local LLMs

  1. Download .gguf weights for local LLMs from a trusted source (e.g., TheBloke's Hugging Face models).
  2. Place the weights in the models/ folder.
  3. Specify the --llm_path argument when running the script: 
    python main.py --image_path data/test.png --llm_path models/local_model.gguf

Contact

For questions or support, please contact:

Citing Words2Contact

If you use Words2Contact, our dataset or part of this code in your research, please cite our paper:

@INPROCEEDINGS{10769902,
  author={Totsila, Dionis and Rouxel, Quentin and Mouret, Jean-Baptiste and Ivaldi, Serena},
  booktitle={2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids)},
  title={Words2Contact: Identifying Support Contacts from Verbal Instructions Using Foundation Models},
  year={2024},
  volume={},
  number={},
  pages={9-16},
  keywords={Accuracy;Large language models;Pipelines;Natural languages;Humanoid robots;Transforms;Benchmark testing;Iterative methods;Surface treatment},
  doi={10.1109/Humanoids58906.2024.10769902}}

Acknowledgements

This research was supported by:

  • CPER CyberEntreprises
  • Creativ’Lab platform of Inria/LORIA
  • EU Horizon project euROBIN (GA n.101070596)
  • France 2030 program through the PEPR O2R projects AS3 and PI3 (ANR-22-EXOD-007, ANR-22-EXOD-004)
Acknowledgments