π₀ piper_stacking BC fine-tune (10k steps, final loss 0.0064)

.gitattributes

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README.md

@@ -0,0 +1,79 @@
+---
+license: other
+library_name: openpi
+pipeline_tag: robotics
+tags:
+- robotics
+- vla
+- manipulation
+- bimanual
+- lerobot
+- openpi
+datasets:
+- axiboai/piper_stacking
+---
+
+# π₀ · piper_stacking (bimanual cube stacking)
+
+π₀ policy fine-tuned with [openpi](https://github.com/Physical-Intelligence/openpi) on
+**[axiboai/piper_stacking](https://huggingface.co/datasets/axiboai/piper_stacking)** — a bimanual PiperX
+robot picking up a red cube and stacking it on a blue cube.
+
+**Prompt:** `stack red cube on blue cube`
+
+## Task & embodiment
+- Robot `piperx_bimanual` — 14-D joint state/action (per arm: 6 joints + gripper × 2)
+- 3 RGB cameras: `cam_front`, `cam_left_wrist`, `cam_right_wrist` (resized to 224×224)
+- Data: 60 demos / 23,087 frames @ 30 Hz, LeRobot v2.1
+
+## Training
+- Base: Physical Intelligence `pi0_base` · full fine-tune (no LoRA) · 3.24B params
+- 10,000 steps · batch 32 · action_horizon 50 · AdamW · cosine LR (warmup 600 → peak 5e-5 → 5e-6) · EMA 0.99
+- State input: continuous · normalization: z-score (mean/std)
+- **Final flow-matching train loss: 0.0064**
+- Hardware: 1 × NVIDIA RTX PRO 6000 Blackwell (96 GB)
+
+## Contents
+- `params/` — EMA inference weights (orbax)
+- `assets/piperx_bimanual/norm_stats.json` — normalization stats (baked from this dataset)
+- `_CHECKPOINT_METADATA`
+- The 29 GB optimizer `train_state/` is intentionally **excluded** (not needed for inference).
+
+## Load & run (openpi)
+Requires the piperx-openpi fork (provides `piperx_policy` + the `pi0_piper_stacking` config).
+
+```python
+import pathlib
+from huggingface_hub import snapshot_download
+from openpi.training import config as _config
+from openpi.policies import policy_config
+
+ckpt = pathlib.Path(snapshot_download("axiboai/pi0_piper_stacking"))
+cfg = _config.get_config("pi0_piper_stacking")
+policy = policy_config.create_trained_policy(cfg, ckpt)
+
+# observation = {"observation.images.cam_front": img, ...cam_left_wrist, ...cam_right_wrist,
+#                "observation.state": state_14d, "prompt": "stack red cube on blue cube"}
+action_chunk = policy.infer(observation)["actions"]   # (50, 14)
+```
+
+If `pi0_piper_stacking` is not yet in your `src/openpi/training/config.py`, add:
+```python
+    TrainConfig(
+        name="pi0_piper_stacking",
+        model=pi0_config.Pi0Config(pi05=False),
+        weight_loader=weight_loaders.CheckpointWeightLoader("gs://openpi-assets/checkpoints/pi0_base/params"),
+        data=LeRobotPiperXBimanualDataConfig(
+            repo_id="axiboai/piper_stacking",
+            base_config=DataConfig(prompt_from_task=True),
+            assets=AssetsConfig(asset_id="piperx_bimanual"),
+            default_prompt="stack red cube on blue cube",
+        ),
+        lr_schedule=_optimizer.CosineDecaySchedule(warmup_steps=600, peak_lr=5e-5, decay_steps=9400, decay_lr=5e-6),
+        num_train_steps=10000, batch_size=32, save_interval=2500, keep_period=2500,
+    ),
+```
+
+## Provenance / license
+Fine-tuned from Physical Intelligence's `pi0_base` checkpoint via openpi; the weights inherit the
+upstream openpi / base-model license. Dataset: axiboai/piper_stacking.

_CHECKPOINT_METADATA

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assets/piperx_bimanual/norm_stats.json

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