pi05_bi task1_all step 10000

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -0,0 +1,88 @@
+---
+license: apache-2.0
+tags:
+  - robotics
+  - openpi
+  - pi05
+  - lerobot
+  - vla
+---
+
+# pi05_bi — task1_all — step 10000
+
+openpi `pi05_bi` checkpoint, snapshot at **training step 10000**.
+Task: *"Use the left hand to pick up the black tube, and then transfer it to the
+right hand and place it into the rack."*
+
+## Data
+
+Merged from three LeRobot v2.1 datasets (30 fps, images embedded in parquet,
+`total_videos=0`):
+
+| source | episodes | frames |
+|---|---|---|
+| `KaiyueChen/task1_01` | 550 | 375,061 |
+| `KaiyueChen/task1_02` | 200 | 130,902 |
+| `KaiyueChen/task1_03` | 200 | 114,987 |
+| **merged** | **950** | **620,950** |
+
+`task1_01` shipped with the placeholder task string `perform manipulation task`
+while 02/03 carried the full description. Since `prompt_from_task=True` feeds
+this string to the model, all episodes were unified to the 02/03 wording at
+merge time — otherwise 58% of the data would train against an uninformative
+prompt.
+
+Split (per-source 10% held out, seed 42): **train 855 / val_seen 95 /
+val_unseen 95**. Normalization statistics were computed over the *train split
+only*.
+
+## Model & training
+
+- 6 camera streams: `camera0`, `camera1`, `tactile_{left,right}_{0,1}`
+- `state_dim=20`, `action_dim=20`, `action_horizon=50`
+- LoRA on the PaliGemma LLM (rank 16) and the action expert (rank 32)
+- **The SigLIP vision tower is fully fine-tuned, not LoRA** — openpi's
+  `get_freeze_filter()` matches `.*llm.*` only, so `PaliGemma/img/*` stays
+  trainable. That is 413M of the 463M trainable parameters (89.8%).
+- batch size 128, FSDP over 2×A100-80GB, `ema_decay=None`
+- `CosineDecaySchedule` defaults: peak_lr 2.5e-5, warmup 1000, decay_steps 30000
+- init from `gs://openpi-assets/checkpoints/pi05_base/params`
+
+One epoch is 4356 steps, so step 10000 ≈ **2.3 epochs**.
+
+## Validation at this checkpoint
+
+Loss is the flow-matching training objective evaluated with `train=False`
+(image augmentation disabled), averaged over a fixed set of batches with a
+fixed rng so successive points differ only by the model.
+
+| step | val_seen | val_unseen |
+|---|---|---|
+| 2000 | 0.0575 | 0.0534 |
+| 4000 | 0.0530 | 0.0501 |
+| 6000 | 0.0497 | 0.0482 |
+| 8000 | 0.0483 | 0.0470 |
+| **10000** | **0.0463** | **0.0465** |
+
+`val_unseen` reached a sixth consecutive new low here, so no overfitting turn
+had occurred. The per-step improvement was shrinking fast, though: 3.8% ->
+2.4% -> 1.1%, so the curve is approaching a plateau.
+
+For context, an earlier run of the same recipe on a *different* dataset
+(`pick_tube`, 1250 episodes) bottomed out at step 8000 and degraded from step
+10000 onward. That did not happen here. Absolute losses are **not** comparable
+across the two datasets — they use different normalization statistics, and
+task1's trajectories are slower and smoother (per-step |Δaction| 0.00052 vs
+0.00079), which lowers the achievable regression loss independently of task
+difficulty.
+
+**Caveat on the numbers:** validation used `val_batches=20` at batch size 128 =
+2560 frames, and episodes here average 653 frames, so each evaluation covers
+only the first ~4 episodes of each split (~5.4%). The *absolute trend* of
+`val_unseen` is meaningful (identical episodes and rng every time), but the
+seen-vs-unseen difference rests on 4 episodes per side and should not be read
+as a generalization gap.
+
+## Contents
+
+Orbax checkpoint: `params/`, `train_state/`, `assets/`, `_CHECKPOINT_METADATA`.

checkpoint/_CHECKPOINT_METADATA

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checkpoint/params/_sharding

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checkpoint/params/array_metadatas/process_0

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checkpoint/train_state/_sharding

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checkpoint/train_state/array_metadatas/process_0

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