Official PyTorch implementation of Finetuning Offline World Models in the Real World (CoRL 2023 Oral)
Paper | Website | Dataset (sim) | Dataset (real)
Install dependencies using conda:
conda env create -f environment.yaml
conda activate fowm
After installing dependencies, you can train an agent by
python src/train_off2on.py task=antmaze-medium-play-v2
Supported tasks from D4RL: antmaze-medium-play-v2, antmaze-medium-diverse-v2, hopper-medium-v2, hopper-medium-replay-v2.
To run experiments on xArm tasks, first download our released offline datasets
python scripts/download_datasets.py
Datasets will be saved at the directory data:
data
├── xarm_lift_medium
├── xarm_lift_medium_replay
├── xarm_push_medium
└── xarm_push_medium_replay
Then start training with
python src/train_off2on.py modality=all task=xarm_lift dataset_dir=data/xarm_lift_medium_replay
You can choose xarm_lift or xarm_push as task and use dataset_dir to specify the offline dataset.
The training script supports both local logging as well as cloud-based logging with Weights & Biases. To use W&B, provide a key by setting the environment variable WANDB_API_KEY=<YOUR_KEY> and add your W&B project and entity details to cfgs/config.yaml.
If you find our work useful in your research, please consider citing with the following BibTeX:
@inproceedings{feng2023finetuning,
title={Finetuning Offline World Models in the Real World},
author={Feng, Yunhai and Hansen, Nicklas and Xiong, Ziyan and Rajagopalan, Chandramouli and Wang, Xiaolong},
booktitle={Proceedings of the 7th Conference on Robot Learning (CoRL)},
year={2023}
}
This repository is licensed under the MIT license. The codebase is based on the original implementations of TD-MPC.