| --- |
| tags: |
| - robotics |
| - humanoid |
| - imitation-learning |
| --- |
| # Delta Flow — Humanoid Benchmark |
|
|
| Delta Flow (ImageWAM / HumanWAM whole-body delta-flow policy) checkpoints trained on Humanoid Benchmark tasks (40 epochs; the last two saved checkpoints are published). |
|
|
| ## Checkpoints |
|
|
| 100 demonstrations per task. Every row is one training run; the epoch folders are its published checkpoints. |
|
|
| | Folder | Task | Robot | Cameras (model input) | Global batch | Proprio augmentation | Checkpoints | |
| |---|---|---|---|---|---|---| |
| | `b6-pedal-bin/` | B6 — pick up a chip barrel, step on the pedal bin, drop it in | Unitree G1 (Inspire hands) | ego_view, chest | — | aligned (GR00T A20 recipe) | `epoch-030`, `epoch-040` | |
| |
| ## What an epoch folder contains |
| |
| Every checkpoint folder (`epoch-XXX/`, or `step-XXXXXX/` for runs trained for a fixed number of steps) is self-contained for evaluation and deployment: weights, model configuration, normalization |
| statistics and `protocol.json` (task, data, input/output dimensions, cameras, batch, steps, augmentation, file list). |
| Simple training records are included for reproducibility (training plan, training log, configs, metrics); optimizer / |
| scheduler / RNG / DeepSpeed resume state is not. `code/` holds the adapter code used for training and inference |
| (`code/VERSIONS.json`: upstream commits and package versions). |
| |
| ## Inputs and augmentation |
| |
| Model inputs are identical across all benchmark models: G1 state 44-D (29 body joint angles + 6 + 6 measured hand values |
| + 3 projected gravity), H2 state 78-D (75 joint angles + 3 projected gravity), no joint velocities. Cameras: G1 ego view + |
| chest; H2 head + chest (A14: head + chest + wrist). The third-person camera recorded in the datasets is for data review |
| only and is never a model input. Each camera is a separate image input, except for Delta Flow and DiT4DiT, which |
| place the views side by side in one frame. |
| Training uses the Humanoid Benchmark online data augmentation spec v2 (15 photometric / geometric / sensor / lighting |
| effects, one of them boosted to 1.5x per sample) with the model's own image augmentation turned off, plus joint-angle |
| bias and noise on the proprioception (column "Proprio augmentation"; the earlier recipe also delayed the |
| proprioception and dropped action-history rows). |
| |
| `legacy/` holds earlier experiments that do NOT follow the benchmark protocol; see `legacy/README.md`. |
| |
