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:
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.