| --- |
| license: apache-2.0 |
| library_name: transformers |
| tags: [robotics, vla, lingbot-vla, lerobot, manipulation] |
| --- |
| |
| # LingBot-VLA 2.0 — Stack-the-cubes ablations |
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| Finetunes of **LingBot-VLA 2.0** (6B, MoE action expert) on Unitree G1 Dex1 dual-arm |
| cube stacking. All runs freeze the vision encoder; distillation teachers stay frozen, |
| current+future depth/DINO query heads train. Muon, lr 1e-4, L1_fm, bounds_99_woclip, |
| absolute actions. |
| |
| | Subfolder | Data | Action space | Steps | Note | |
| |---|---|---|---|---| |
| | `joint100/` | 100ep | joint (14 arm + 2 grip) | 20000 (0.42 epoch) | vision frozen, 2xA100 | |
| | `eef/` | 150ep (v1+v2) | EEF pose (14 + 2 grip) | 20000 (0.29 epoch) | vision frozen, 2xA100 | |
| | `eef100/` | 100ep | EEF pose (14 + 2 grip) | 20000 (0.42 epoch) | vision frozen, 2xA100 | |
| | `eef100_10ep/` | 100ep | EEF pose (14 + 2 grip) | 59979 (**10 full epochs**) | vision frozen, GBS=16, 2xB200 | |
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| Ablation axes: `joint100` vs `eef100` = joint vs EEF (same data); `eef` vs `eef100` |
| = mixed 150ep vs clean 100ep; `eef100` vs `eef100_10ep` = training exposure |
| (0.42 epoch vs 10 full epochs of the same 100ep EEF data — the original runs never |
| completed even one full pass over their training set). Each `*/global_step_*/` |
| holds deployable HF weights (`model-0000x-of-00006.safetensors` + tokenizer/config). |
| EEF models output end-effector poses — the client must IK them back to joints |
| (see g1-client `main_eef.py`). |
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