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0000000000000000000000000000000000000000..73d12de427d763e4312b3604a395817c4ffcc3b8 --- /dev/null +++ b/pi_behavior_100t_meta_ft_task77_noda3/README.md @@ -0,0 +1,31 @@ +# PiBehavior — 100-task meta → single-task finetune: `installing_a_modem` (BEHAVIOR-1K 2026) + +A flow-matching vision-language-action (VLA) policy for the **BEHAVIOR-1K 2026 challenge**, finetuned from the **100-task meta checkpoint** on the single task **`installing_a_modem`** (activity index **77**). RGB-only (no DA3 depth branch). + +## Model +- **Architecture:** PiBehavior (the 2025 #1 IliaLarchenko `behavior_224_rgb` architecture), pi0-style flow-matching VLA. + - VLM backbone: **PaliGemma (gemma_2b)** · action expert: **gemma_300m** · compute dtype **bf16**. + - 100-task conditioning tables (task + stage embeddings); vision backbone frozen. +- **Init:** the **100-task meta checkpoint** (`meta100-1epoch`, step **69999**). +- **Finetuned on 1 task:** `77` **installing_a_modem**. +- **Recipe:** 4×H200, batch **128**, FSDP=1, **2 epochs** (20,000 steps; final checkpoint = step **19999**). LR ramp-cosine `1e-8 → 5e-5` (by 1k) `→ 1e-6` (by 20k). **qvel-fixed** norm stats (bundled), fast tokenizer from the meta checkpoint. +- **Data:** `b1k-224x224-gop8-fixed` (224×224 RGB, GOP8), the `installing_a_modem` episodes. + +## Contents +- `params/` — Orbax model parameters (bf16). Load with the openpi `PiBehaviorWeightLoader`. +- `assets/IliaLarchenko/behavior_224_rgb/` — **`norm_stats.json`** (qvel-fixed) + **`fast_tokenizer/`**. Required for eval. +- (Optimizer `train_state` is NOT included — inference/eval release.) + +## Inputs / Outputs +**Observation (input):** 3 RGB views (`base_0_rgb`, `left_wrist_0_rgb`, `right_wrist_0_rgb`, each 224×224×3 uint8) + `state` (32 float, normalize with `norm_stats["state"]`) + `prompt` (tokenized by the bundled fast tokenizer) + `task_index` = **77**. + +**Action (output):** `action` = **32-dim × 30-step** chunk (horizon 30), flow-sampled then **de-normalized with `norm_stats["actions"]`**. Meaningful dims (23 of 32): base vel (3), trunk (4), left arm (7), right arm (7), grippers (2). + +## How to evaluate (properly) +1. **Load** `params/` with the `pi_behavior` config (`num_tasks=100`, gemma_2b + gemma_300m, `da3=None`). +2. **Assets:** point `assets_base_dir` at the bundled `assets/` (these **qvel-fixed** `norm_stats.json` + `fast_tokenizer`) — do **not** substitute upstream/2025 norm stats. +3. **Per step:** 3 RGB views + `state` (normalized) + `prompt` + `task_index=77`; sample the 30-step chunk (10–15 flow steps), de-normalize, execute (receding-horizon). +4. **Benchmark:** BEHAVIOR-1K sim on **installing_a_modem**; report task **success rate**. Single-task specialist — evaluate only task 77. + +## Notes +- RGB-only baseline (no GT-depth / DA3). Its DA3 counterpart is a separate checkpoint. diff --git a/pi_behavior_100t_meta_ft_task77_noda3/_CHECKPOINT_METADATA b/pi_behavior_100t_meta_ft_task77_noda3/_CHECKPOINT_METADATA new file mode 100644 index 0000000000000000000000000000000000000000..12d6d5daebdbe5f49062a1ed89f565b9273f402f --- /dev/null +++ b/pi_behavior_100t_meta_ft_task77_noda3/_CHECKPOINT_METADATA @@ -0,0 +1 @@ +{"item_handlers": {"assets": "b1k.training.checkpoints.CallbackHandler", "params": "orbax.checkpoint._src.handlers.pytree_checkpoint_handler.PyTreeCheckpointHandler", "train_state": "orbax.checkpoint._src.handlers.pytree_checkpoint_handler.PyTreeCheckpointHandler"}, "metrics": {}, "performance_metrics": {}, "init_timestamp_nsecs": 1787739298674429019, "commit_timestamp_nsecs": 1787739420910149430, "custom_metadata": {}} \ No newline at end of file diff --git a/pi_behavior_100t_meta_ft_task77_noda3/assets/IliaLarchenko/behavior_224_rgb/norm_stats.json 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