LIV-robotics 0.1.2

LIV: Language-Image Representations and Rewards for Robotic Control

LIV: Language-Image Representations and Rewards for Robotics Control

International Conference on Machine Learning (ICML), 2023

[Project Page] [Arxiv][Paper] [Dataset] [HuggingFace Model Page]

Jason Yecheng Ma¹, Vikash Kumar², Amy Zhang², Osbert Bastani¹, Dinesh Jayaraman¹

¹University of Pennsylvania, ²Meta AI

This is the official repository for LIV, an algorithm for pre-training, fine-tuning, and reward learning for language-conditioned robotic control. This repository contains examples for using the pre-trained LIV model as well as training LIV from scratch using any custom video dataset.

Table of Contents

Installation

LIV Usage Examples

LIV Training

LIV Fine-Tuned Reward Curve Visualization

Installation

Create a conda environment where the packages will be installed.

conda create --name liv-env python=3.9
conda activate liv-env

Then, in the root directory of this repository, run:

pip install -e .;
cd liv/models/clip; pip install -e .;

LIV Usage Examples

Quick start:

from liv import load_liv
liv = load_liv()
liv.eval()

The following code snippet demonstrates an example for loading the model as well as performing inference on an example image and text (python liv/examples/liv_static.py):

import clip
import torch
import torchvision.transforms as T
from PIL import Image 

from liv import load_liv

device = "cuda" if torch.cuda.is_available() else "cpu"

# loading LIV
liv = load_liv()
liv.eval()
transform = T.Compose([T.ToTensor()])

# pre-process image and text
image = transform(Image.open("sample_video/frame_0000033601.jpg")).unsqueeze(0).to(device)
text = clip.tokenize(["open microwave", "close microwave", "wipe floor"]).to(device)

# compute LIV image and text embedding
with torch.no_grad():
    img_embedding = liv(input=image, modality="vision")
    text_embedding = liv(input=text, modality="text")

# compute LIV value
img_text_value = liv.module.sim(img_embedding, text_embedding)
# Output: [ 0.1151, -0.0151, -0.0997]

We have also included an example for generating multi-modal reward curves on text-annotated videos. You can try it here:

cd liv/examples
python liv_example.py

This should generate the following animated reward curves in liv/examples:

Training LIV Representation

Our codebase supports training LIV on both the EpicKitchen dataset that was used in pre-training our released LIV model as well as any custom video dataset. The video dataset directory should use the following structure:

my_dataset_path/
    video0/
        0.png
        1.png
        ...
    video1/
    video2/
    ...
    manifest.csv

The manifest.csv file should contain rows of directory, text, num_frames, which indicates the absolute path, text annotation, and length of each video, respectively.

Then, you can use LIV to fine-tune a pre-trained vision-language model (e.g., LIV, CLIP) on your dataset by (1) adding a <my_dataset_name>.yaml file that specifies the dataset name and path in /cfgs/dataset:

python train_liv.py training=finetune dataset=my_dataset_name

We have provided an example of LIV fine-tuning using the realrobot dataset we used in the paper.

For EpicKitchen or equivalent large-scale pre-training, we suggest using config pretrain.yaml (the config for the released LIV model):

python train_liv.py  training=pretrain dataset=epickitchen

Each training run will generate a training run folder under train_liv_realrobot and the reward curves for intermediate model snapshots will be saved in \reward_curves of the run folder.

Multi-Modal Reward Curve Generation

We can use the same training code to also only generate the (animated) reward curves by setting eval=True

python train_liv.py eval=true dataset=epickitchen animate=True

We can also specify a model path (e.g., snapshot.pt saved in a run folder) and generate reward curves on the dataset the model is LIV fine-tuned with:

python train_liv.py eval=True load_snap=PATH_TO_LIV_MODEL dataset=realrobot animate=True 

In the run folder, you should see animated reward curves like the following:

License

The source code in this repository is licensed under the CC BY-NC 4.0 License.

Citation

If you find this repository or paper useful for your research, please cite

@article{ma2023liv,
  title={LIV: Language-Image Representations and Rewards for Robotic Control},
  author={Ma, Yecheng Jason and Liang, William and Som, Vaidehi and Kumar, Vikash and Zhang, Amy and Bastani, Osbert and Jayaraman, Dinesh},
  journal={arXiv preprint arXiv:2306.00958},
  year={2023}
}

Ackowledgements

Parts of this code are adapted from VIP and CLIP.