| --- |
| license: mit |
| tags: |
| - diffusion |
| - robotics |
| - planning |
| - safe-control |
| --- |
| |
| # JM2D checkpoints |
|
|
| Pre-trained checkpoints for **Joint Model-based Model-free Diffusion for Planning with Constraints** (CoRL 2025). |
|
|
| [[Project page]](https://jm2d-corl25.github.io/) [[ArXiv]](https://arxiv.org/abs/2509.08775) [[Code]](https://github.com/goldenhazard/JM2D) |
|
|
| Wonsuhk Jung\*, Utkarsh A. Mishra\*, Nadun Ranawaka Arachchige, Yongxin Chen, Danfei Xu, Shreyas Kousik — Georgia Institute of Technology |
|
|
| Each folder belongs to one branch of the [code repository](https://github.com/goldenhazard/JM2D); that branch's `scripts/download_checkpoints.py` downloads |
| its folder and checks the md5 sums below. The toy-domain checkpoints are small and ship with the `public_toy` branch. |
| The hardware branch (`public_hardware`) is a guide for running JM2D on your own robot and has no checkpoint. |
|
|
| ## Files |
|
|
| | Folder | Branch | Files | Size | |
| |---|---|---|---| |
| | `pointmaze/` | `public_robomimic` | `maze2d_large_ep{1250,1500,1750,1900,2000}.pth`: robomimic diffusion policies on D4RL `maze2d-large-dense-v1` (the 5 checkpoints of paper Fig. 4) | 5 × 524 MB | |
| | `d3il/data/avoiding/` | `public_d3il` | `env_*.pkl` (96 files) + `MD5SUMS`: the D3IL Avoiding demonstrations, needed for training and for evaluation (data normalizer) | 2.6 MB | |
| | `d3il/avoiding-d3il/H8_K20_Dmodels.GaussianDiffusion/<seed>/` | `public_d3il` | `state_best.pt` + config pickles: Diffuser-style planners on D3IL-Avoiding, seeds 0–4 (paper Tables 2 and 3) | 5 × 32 MB | |
| | `pointmaze/dataset/` | `public_robomimic` | `maze2d_large_dense_v1.hdf5`: the D4RL `maze2d-large-dense-v1` dataset converted to robomimic format, used to train the PointMaze policies | 303 MB | |
|
|
| | File | md5 | |
| |---|---| |
| | `pointmaze/maze2d_large_ep1250.pth` | `98614093985811a0dbcbb4a66b3ccfc4` | |
| | `pointmaze/maze2d_large_ep1500.pth` | `000b0f287307bd98289cc9b28479566c` | |
| | `pointmaze/maze2d_large_ep1750.pth` | `a7fd3fe32e1b35723dde1c566c61f1b9` | |
| | `pointmaze/maze2d_large_ep1900.pth` | `a756af86fe2070c2e77358ca380ccc44` | |
| | `pointmaze/maze2d_large_ep2000.pth` | `7f76546cb300ec7577c475d1f3b40f97` | |
| | `d3il/.../0/state_best.pt` | `8446a72feebe0e9b1839bc5cb48f448e` | |
| | `d3il/.../1/state_best.pt` | `c09dd9a85fcf435201d8e61835d053f0` | |
| | `d3il/.../2/state_best.pt` | `4c55388894fdf2ebeb22206678125ac3` | |
| | `d3il/.../3/state_best.pt` | `7d219c1b2df29557297bde3ab561b784` | |
| | `d3il/.../4/state_best.pt` | `7238d20b674872da4a8d88e8469a5a3b` | |
| | `pointmaze/dataset/maze2d_large_dense_v1.hdf5` | `aa4be41d7444c0bdc8fcf48707e11c84` | |
|
|
| ## Download |
|
|
| From a checkout of the matching branch: |
|
|
| ```bash |
| python scripts/download_checkpoints.py |
| ``` |
|
|
| or directly: |
|
|
| ```bash |
| hf download RoboWonsuhk/JM2D --include "pointmaze/*" --local-dir checkpoints_hf |
| ``` |
|
|
| The PointMaze dataset is a converted copy of the D4RL dataset (Fu et al., 2020, https://github.com/Farama-Foundation/D4RL), redistributed under its CC BY 4.0 license. |
|
|
| The D3IL Avoiding demonstrations are an unmodified copy from D3IL (Jia et al., 2024, https://github.com/ALRhub/d3il, MIT License). |
|
|
| The files are PyTorch pickles; load them only with the code of the matching branch. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{jung2025joint, |
| title={Joint Model-based Model-free Diffusion for Planning with Constraints}, |
| author={Wonsuhk Jung and Utkarsh Aashu Mishra and Nadun Ranawaka Arachchige and Yongxin Chen and Danfei Xu and Shreyas Kousik}, |
| booktitle={9th Annual Conference on Robot Learning}, |
| year={2025}, |
| url={https://openreview.net/forum?id=E9t1ekt6W9} |
| } |
| ``` |
|
|
