# PushT Dataset

Video + action data from the [gym-pusht](https://github.com/huggingface/gym-pusht) environment. Three splits:

| 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 |

## File format

Each `.npz` file contains multiple episodes. Load with:

```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"
```

- `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)