| # PushT Dataset |
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| Video + action data from the [gym-pusht](https://github.com/huggingface/gym-pusht) environment. Three splits: |
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| | Split | Episodes | Avg steps/ep | Hours | Description | |
| |-------|----------|-------------|-------|-------------| |
| | `smooth/` | ~38,900 | 300 | ~324 hrs | Random smooth movement (Ornstein-Uhlenbeck process) | |
| | `goal/` | ~8,700 | 298 | ~73 hrs | Heuristic goal-directed policy (keypoint matching) | |
| | `expert/` | ~21,800 | 228 | ~138 hrs | Pretrained diffusion policy, ~74% success rate | |
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| ## File format |
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| Each `.npz` file contains multiple episodes. Load with: |
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|
| ```python |
| import numpy as np |
| data = np.load("smooth/smooth_0000_00.npz", allow_pickle=True) |
| n = int(data["num_trajectories"]) # number of episodes in this file |
| for i in range(n): |
| frames = data[f"frames_{i}"] # (T+1, 96, 96, 3) uint8 — RGB pixel observations |
| actions = data[f"actions_{i}"] # (T, 2) float32 — agent target position [x, y] in [0, 512] |
| rewards = data[f"rewards_{i}"] # (T,) float32 — coverage ratio, 1.0 = solved |
| policy = str(data[f"policy_{i}"]) # "smooth", "goal", or "expert" |
| ``` |
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| - `frames` has one more entry than `actions` (initial frame before first action) |
| - `frames[t]` is the observation *before* `actions[t]` is taken |
| - `frames[t+1]` is the observation *after* `actions[t]` is taken |
| - The environment runs at 10 Hz (0.1s per step) |
| - An episode is "solved" when `rewards[t] >= 0.95` (the T-block covers >95% of the goal) |
