README.md

In-Context Imitation Learning via Next-Token Prediction

by Max (Letian) Fu*, Huang Huang*, Gaurav Datta*, Lawrence Yunliang Chen, William Chung-Ho Panitch, Fangchen Liu, Hui Li, and Ken Goldberg at UC Berkeley and Autodesk (*equal contribution).

[Paper] | [Project Page] | [Checkpoints] | [Dataset] | [Citation]

This repo contains the checkpoints for In-Context Imitation Learning via Next-Token Prediction. We investigate how to bring few-shot, in-context learning capability that exists in next-token prediction models (i.e. GPT) into real-robot imitation learning policies.

In particular, we store the pre-trained vision encoder and ICRT model separately. Please find them in encoder, ICRT, and ICRT-Llama7B.

Please refer to the code on installing the repo, training and inferencing the model.

Dataset Structure

ICRT-MT
├── merged_data_part1.hdf5
│   ├── episode_1
│   │   ├── observation
│   │       ├── exterior_image_1_left
│   │       └── exterior_image_2_left
│   │       └── wrist_image_left
│   │       └── cartesian_position
│   │       └── gripper_position
│   │       └── joint_position
│   │   ├── action
│   │       ├── cartesian_velocity
│   │       └── gripper_velocity
│   │       └── joint_velocity
│   │       └── cartesian_position
│   │       └── gripper_position
│   │       └── joint_position
│   │   ├── language_instruction
│   │   ├── language_instruction_2
│   │   ├── language_instruction_3
│   │   ├── language_embedding
│   │   ├── language_embedding_2
│   │   ├── language_embedding_3
│   │   ...
│   ├── episode_2
│   │   ...
│   └── episode_3
│       ...
└── merged_data_part1_keys.json
...

Citation

Please give us a star 🌟 on Github to support us!

Please cite our work if you find our work inspiring or use our code in your work:

@article{fu2024icrt,
    title={In-Context Imitation Learning via Next-Token Prediction}, 
    author={Letian Fu and Huang Huang and Gaurav Datta and Lawrence Yunliang Chen and William Chung-Ho Panitch and Fangchen Liu and Hui Li and Ken Goldberg},
    journal={arXiv preprint arXiv:2408.15980},
    year={2024}
}