| --- |
| license: mit |
| tags: |
| - robotics |
| - reinforcement-learning |
| - world-model |
| - robomimic |
| - jax |
| --- |
| |
| # Pretrained state world models |
|
|
| Anonymous supplementary checkpoints for the accompanying double-blind |
| submission. These state-space dynamics models are used for tree search on |
| Robomimic low-dimensional tasks. |
|
|
| Each `.pkl` contains the Flax parameter tree for a frozen dynamics module. It |
| does not contain policy, critic, optimizer, dataset, or evaluation outputs. |
| Use the checkpoint whose seed matches the training run. |
|
|
| | Directory | Task and dataset | Pretraining steps | |
| | --- | --- | ---: | |
| | `lift_fixed10/` | Lift, PH fixed-10 subset | 100,000 | |
| | `can_mh/` | Can, MH | 100,000 | |
| | `square/` | Square, PH | 100,000 | |
| | `tool_hang_inlinewm200k/` | Tool Hang, PH | 200,000 | |
|
|
| Download all files with: |
|
|
| ```bash |
| hf download qlearningwithworldmodels/anonymous-state-world-models \ |
| --include "*.pkl" \ |
| --local-dir checkpoints |
| ``` |
|
|
| Then pass a matching local file to `WM_CKPT` or |
| `--wm_dynamics_ckpt_path`. Verify downloads against `SHA256SUMS` before |
| loading. Python pickle files can execute code during deserialization; only |
| load files obtained from this repository with matching hashes. |
|
|
| Author and archival links are intentionally omitted during double-blind |
| review and will be added after the review period. |
|
|
