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act-pantheon-yam-screwdriver-vn

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ACT policy + VN-ablation research doc

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  1. README.md +80 -0
  2. comparison.png +0 -0
  3. config.json +79 -0
  4. model.safetensors +3 -0
  5. policy_postprocessor.json +32 -0
  6. policy_postprocessor_step_0_unnormalizer_processor.safetensors +3 -0
  7. policy_preprocessor.json +72 -0
  8. policy_preprocessor_step_3_normalizer_processor.safetensors +3 -0
  9. train_config.json +209 -0
  10. vn_stats.json +3449 -0
README.md ADDED
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+ # ACT — Pantheon YAM 'swap screwdriver head' — **Velocity-Normalized**
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+
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+ ACT (Action-Chunking Transformer) policy for a bimanual YAM manipulation task, trained as
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+ one arm of a **Velocity-Normalization (VN) ablation**. This checkpoint: VN.
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+
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+ Trained on **velocity-normalized** demonstrations (VN re-times each demo to a consistent speed profile: idle removed, fast motion slowed, cruising aligned).
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+
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+ > **Companion model:** [atharva-pantheon/act-pantheon-yam-screwdriver-naive](https://huggingface.co/atharva-pantheon/act-pantheon-yam-screwdriver-naive) — the other arm
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+ > of the ablation (same data, same config, uniform 30→10 Hz downsample).
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+
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+ ---
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+
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+ ## Research summary
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+
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+ **Question.** Does Velocity-Normalization (VN) preprocessing — re-timing teleop demos so a
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+ policy sees a consistent end-effector speed distribution — change what an ACT policy learns,
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+ holding the underlying demonstrations fixed?
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+
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+ **Setup.** A controlled ablation: two ACT policies, identical architecture, hyperparameters,
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+ and seed; the **only** difference is how source frames are selected when building the 10 Hz
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+ training set.
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+
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+ | | this model (VN) | companion |
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+ |---|---|---|
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+ | frame selection | velocity-normalized 30→10 Hz | uniform 30→10 Hz downsample |
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+
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+ ### Data
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+ - **Source:** `pantheon-lerobot-mix/data_pi05_ready/pantheon_yam` (Pantheon mid-training mix, LeRobot v2.0).
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+ - **Task:** *"swap the tool head on the screwdriver"* (task_index 16) — **379 episodes, ~5.2 h**,
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+ the task with the most episodes. Bimanual YAM, 14-D joints (zero-padded to 20), 3 cameras
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+ (top / wrist-L / wrist-R), 30 fps.
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+ - Both training sets built at **224×224, 10 Hz**, same writer, same action labeling. VN frame
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+ selection is the sole variable.
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+
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+ ### Velocity Normalization (VN)
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+ Implementation: <https://github.com/vovw/vn-pipeline>. End-effector speed via forward
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+ kinematics (pinocchio, YAM URDF `link_6`), bimanual speed = max over arms. Two stages:
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+ - **Stage 1 (inter-episode):** align each episode's cruising speed (30th-pct) toward the median (clamp 0.75–1.5×).
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+ - **Stage 2 (intra-episode):** a smooth monotonic speed map H(s) that slows the fast tail; gripper-event windows and trailing idle preserved.
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+
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+ Applied to this task: breakpoints m=0.024, M=0.162 m/s; **566,204 source frames → 211,501 VN frames**
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+ (1.12× duration ratio; 1.1% idle dropped; Stage-1 factor median 1.0×, range 0.75–1.5). The naive
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+ baseline is a plain uniform 3× downsample (≈189k frames, idle kept).
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+
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+ ### Training
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+ ACT, ResNet18 (ImageNet) vision backbone, `chunk_size=30`, `n_action_steps=30`, `n_obs_steps=1`,
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+ 224×224, batch 64, lr 1e-5, **10,000 steps** (≈6.7 epochs), seed 1000. A100 80 GB, both runs concurrent.
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+ Logged to W&B project `vn-act-screwdriver`.
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+
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+ ### Result
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+ | run | start loss | final loss (L1+KL) |
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+ |---|---|---|
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+ | VN | 5.27 | **0.294** |
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+ | naive (no-VN) | 5.19 | **0.310** |
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+
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+ ![loss comparison](comparison.png)
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+
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+ Both converge tightly (same demonstrations). **Training loss is not the verdict** — VN's intended
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+ benefit is *consistent execution speed* at inference, which requires a robot/sim rollout to
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+ evaluate. What this run establishes: both policies train cleanly to convergence on identical
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+ data with VN frame-selection as the only difference.
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+
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+ ## Files
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+ - `model.safetensors` — ACT weights (~52 M params)
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+ - `config.json`, `train_config.json` — policy + training config
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+ - `policy_preprocessor*/policy_postprocessor*` — input/output normalization (required to run)
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+ - `comparison.png` — VN vs no-VN training-loss curves
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+
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+ ## Usage (LeRobot)
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+ ```python
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+ from lerobot.policies.act.modeling_act import ACTPolicy
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+ policy = ACTPolicy.from_pretrained("atharva-pantheon/act-pantheon-yam-screwdriver-vn")
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+ ```
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+
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+ ## Notes / provenance
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+ - State/action are 20-D (14-D YAM joints zero-padded; `valid_action_dims=14`).
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+ - Built via a v2.0→v3.0 adapter (the vn-pipeline `run_vn.py` targets v3.0 input).
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+ - Engineering note: on the 128-core training box, capping `OMP_NUM_THREADS` was essential —
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+ uncapped, `ToTensor` cost ~236 ms/image (thread-dispatch overhead) vs 0.27 ms capped (~1000×),
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+ which otherwise starved the GPU.
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