ACT — Humanoid Benchmark
ACT (Action Chunking Transformer, ResNet18 backbone, CVAE; Psi0 baselines/act) checkpoints trained on Humanoid Benchmark tasks.
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 | 696 | earlier recipe (with delay/drop) | epoch-020 |
a3-fridge-open/ |
A3 — open the fridge door | Unitree G1 (Inspire hands) | ego_view, chest | 696 | earlier recipe (with delay/drop) | epoch-010, epoch-020 |
a7-cabinet-close/ |
A7 — close the cabinet door | Unitree G1 (Inspire hands) | ego_view, chest | 696 | earlier recipe (with delay/drop) | epoch-010, epoch-020 |
a10-trolley-push/ |
A10 — push a trolley | Unitree G1 (Inspire hands) | ego_view, chest | 696 | earlier recipe (with delay/drop) | epoch-010, epoch-020 |
a20-basket-transfer/ |
A20 — transfer a basket | Unitree G1 (Inspire hands) | ego_view, chest | 696 | earlier recipe (with delay/drop) | epoch-010, epoch-020 |
b7-black-clothes-hamper/ |
B7 — take the black garment off the hanger and drop it into the hamper | Unitree G1 (Inspire hands) | ego_view, chest | 696 | earlier recipe (with delay/drop) | epoch-010, epoch-020 |
a18-pour-water/ |
A18 — pour water from a pitcher into a glass | H2 (Sharpa hands) | head, chest | 696 | earlier recipe (with delay/drop) | epoch-010, epoch-020 |
a17-unscrew-bottle/ |
A17 — unscrew the cap of a bottle | H2 (Sharpa hands) | head, chest | 696 | earlier recipe (with delay/drop) | epoch-010, epoch-020 |
What an epoch folder contains
Every epoch-XXX/ folder 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).