| --- |
| pretty_name: GlanceWAM reproduction bundle |
| license: other |
| task_categories: |
| - robotics |
| tags: |
| - robotics |
| - vision-language-action |
| - world-model |
| - libero |
| - robocasa |
| - arxiv:2608.23927 |
| --- |
| |
| # GlanceWAM reproduction bundle |
|
|
| **Paper**: [GlanceWAM: Sparse Test-Time Imagination for World-Action Models](https://huggingface.co/papers/2608.23927) |
|
|
| Everything needed to reproduce the GlanceWAM results on LIBERO and RoboCasa kitchen. |
|
|
| ``` |
| datasets/ LeRobot v3 datasets, UMT5 text caches included (glancewam_cache/) |
| checkpoints/ released checkpoints, one directory per run |
| ``` |
|
|
| Point the code at this directory: |
|
|
| ```bash |
| export DATA_ROOT=/data/glancewam_release/datasets # training + precompute |
| ln -s /data/glancewam_release/checkpoints <repo>/results/Checkpoints |
| ``` |
|
|
| ## Checkpoints |
|
|
| | Directory | Benchmark | Reported | |
| |---|---|---| |
| | `glancewam_robocasa_kitchen` | RoboCasa kitchen, 24 tasks x 50 episodes | **0.721** | |
| | `glancewam_libero` | LIBERO 4-in-1, 4 suites x 500 episodes | **0.989** | |
|
|
| Kitchen run-to-run noise is ~0.02 (the environment is paired but the policy is unseeded), so |
| treat anything within ~+/-0.02 as a match. LIBERO is saturated; differences under ~0.005 are noise. |
|
|
| ## Datasets |
|
|
| | Directory | Used by | |
| |---|---| |
| | `libero_{spatial,object,goal,10}_no_noops_1.0.0_lerobot` | LIBERO (mixture `libero_all`) | |
| | `robocasa_cosmos_kitchen/` | RoboCasa kitchen, 24 per-task datasets (mixture `robocasa_kitchen_all`) | |
|
|
| Each dataset already carries its precomputed UMT5 text cache under |
| `<dataset>/glancewam_cache/t5/Skywork_SkyReels-V2-DF-1.3B-540P-Diffusers_L512`, so training can run |
| with `RESIDENT_TEXT_TABLE=True` (the default) without running the precompute step first. |
|
|
| The dataset trees are hard-linked from `/data/lerobot_v3`, so they cost no extra disk on this |
| machine; copying the directory elsewhere produces independent full copies. |
|
|
| ## Code |
|
|
| <https://github.com/linhanwang/GlanceWAM> |
|
|
| ```bash |
| # everything (21 GB) |
| hf download LinhanWang/GlanceWAM --repo-type dataset --local-dir ./glancewam_bundle |
| |
| # or just one benchmark |
| hf download LinhanWang/GlanceWAM --repo-type dataset --local-dir ./glancewam_bundle \ |
| --include "checkpoints/glancewam_libero/*" "datasets/libero_*" # 5.0 GB |
| hf download LinhanWang/GlanceWAM --repo-type dataset --local-dir ./glancewam_bundle \ |
| --include "checkpoints/glancewam_robocasa_kitchen/*" \ |
| "datasets/robocasa_cosmos_kitchen/*" # 16.2 GB |
| |
| # then, from the code checkout |
| mkdir -p results |
| ln -s /abs/path/to/glancewam_bundle/datasets results/Datasets |
| ln -s /abs/path/to/glancewam_bundle/checkpoints results/Checkpoints |
| ``` |
|
|
| ## Attribution |
|
|
| The datasets here are derived from third-party releases and remain subject to their original |
| terms: **LIBERO** (Lifelong Robot Learning, MIT) and the **RoboCasa** kitchen task suite as |
| distributed by NVIDIA's cosmos-policy release (RoboCasa Team, MIT). The checkpoints are fine-tuned |
| from **SkyReels-V2-DF-1.3B-540P** (Skywork) and inherit that model's terms. The GlanceWAM code |
| itself is MIT-licensed; see the code repository. |
|
|
