| --- |
| license: mit |
| tags: |
| - robotics |
| - imitation-learning |
| - manipulation |
| - pi0 |
| - vla |
| --- |
| |
| # cup_pnp β yellow-cup pick-and-place policies |
| |
| Four behavior-cloning policies for the task **"pick up a yellow cup and place it |
| on a baking tray,"** trained from egocentric human video reconstructed into |
| robot demonstrations. This is a 2Γ2 ablation: |
| |
| | architecture Γ observation | **current (raw tray)** | **sim-tray** | |
| |---|---|---| |
| | **Ο0.5** (LoRA on `pi05_base`) | `pi05_current_tray/` | `pi05_sim_tray/` | |
| | **ABC-DiT** (from scratch) | `abc_dit_current_tray.pt` | `abc_dit_sim_tray.pt` | |
|
|
| - **Action / state space:** 28-D β right arm only (3-slide + 3-hinge wrist + 22 |
| finger joints), absolute joint targets. |
| - **Observation:** single rendered robot-view RGB (224Γ224). |
| - **sim-tray** variant re-renders the tray from its simulation mesh (inpainted |
| over the original footage); the **current** variant keeps the tray as raw |
| footage. Actions are identical across the two variants β only the observation |
| pixels differ. |
|
|
| ## Results β held-out action-prediction MSE (normalized, 10-step horizon; lower is better) |
|
|
| | policy | current (raw tray) | sim-tray | |
| |---|---|---| |
| | **Ο0.5** | **0.021** | **0.021** | |
| | **ABC-DiT** | **0.345** | **0.318** | |
|
|
| Ο0.5 (pretrained VLA + LoRA) is ~16Γ more accurate than ABC-DiT (trained from |
| scratch with a randomly-initialized visual backbone). The sim-tray processing is |
| neutral for Ο0.5 and a small improvement for ABC-DiT. |
|
|
| ## Contents |
|
|
| - `pi05_current_tray/`, `pi05_sim_tray/` β Ο0.5 checkpoints (orbax `params/` + |
| `assets/` norm-stats). Load with [openpi](https://github.com/Physical-Intelligence/openpi) |
| `policy_config.create_trained_policy`. Config: `pi05_cup_v1` / `pi05_cup_v2`. |
| - `abc_dit_current_tray.pt`, `abc_dit_sim_tray.pt` β ABC-DiT checkpoints |
| (`{model, step, norm_stats}` + optimizer). Load with |
| [ABC](https://github.com/amazon-far/abc). state/action dim = 28, camera-keys = `top`. |
|
|
| Trained on 80 verified reconstructed demonstrations. See the project report for |
| data provenance and method details. |
|
|