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  1. pusht_dataset/README.md +32 -0
  2. pusht_dataset/expert/expert_0000.npz +3 -0
  3. pusht_dataset/expert/expert_0001.npz +3 -0
  4. pusht_dataset/expert/expert_0002.npz +3 -0
  5. pusht_dataset/expert/expert_0003.npz +3 -0
  6. pusht_dataset/expert/expert_0004.npz +3 -0
  7. pusht_dataset/expert/expert_0005.npz +3 -0
  8. pusht_dataset/expert/expert_0006.npz +3 -0
  9. pusht_dataset/expert/expert_0007.npz +3 -0
  10. pusht_dataset/expert/expert_0008.npz +3 -0
  11. pusht_dataset/expert/expert_0009.npz +3 -0
  12. pusht_dataset/expert/expert_0010.npz +3 -0
  13. pusht_dataset/expert/expert_0011.npz +3 -0
  14. pusht_dataset/expert/expert_0012.npz +3 -0
  15. pusht_dataset/expert/expert_0013.npz +3 -0
  16. pusht_dataset/expert/expert_0014.npz +3 -0
  17. pusht_dataset/expert/expert_0015.npz +3 -0
  18. pusht_dataset/expert/expert_0016.npz +3 -0
  19. pusht_dataset/expert/expert_0017.npz +3 -0
  20. pusht_dataset/goal/goal_0000.npz +3 -0
  21. pusht_dataset/goal/goal_0001.npz +3 -0
  22. pusht_dataset/goal/goal_0002.npz +3 -0
  23. pusht_dataset/goal/goal_0003.npz +3 -0
  24. pusht_dataset/goal/goal_0004.npz +3 -0
  25. pusht_dataset/goal/goal_0006.npz +3 -0
  26. pusht_dataset/goal/goal_0007.npz +3 -0
  27. pusht_dataset/goal/goal_0008.npz +3 -0
  28. pusht_dataset/goal/goal_0009.npz +3 -0
  29. pusht_dataset/goal/goal_0010.npz +3 -0
  30. pusht_dataset/goal/goal_0011.npz +3 -0
  31. pusht_dataset/goal/goal_0013.npz +3 -0
  32. pusht_dataset/goal/goal_0014.npz +3 -0
  33. pusht_dataset/goal/goal_0015.npz +3 -0
  34. pusht_dataset/goal/goal_0016.npz +3 -0
  35. pusht_dataset/goal/goal_0017.npz +3 -0
  36. pusht_dataset/goal/goal_0018.npz +3 -0
  37. pusht_dataset/goal/goal_0019.npz +3 -0
  38. pusht_dataset/goal/goal_0020.npz +3 -0
  39. pusht_dataset/goal/goal_0021.npz +3 -0
  40. pusht_dataset/goal/goal_0022.npz +3 -0
  41. pusht_dataset/goal/goal_0023.npz +3 -0
  42. pusht_dataset/goal/goal_0026.npz +3 -0
  43. pusht_dataset/goal/goal_0027.npz +3 -0
  44. pusht_dataset/goal/goal_0028.npz +3 -0
  45. pusht_dataset/goal/goal_0029.npz +3 -0
  46. pusht_dataset/goal/goal_0030.npz +3 -0
  47. pusht_dataset/goal/goal_0031.npz +3 -0
  48. pusht_dataset/goal/goal_0032.npz +3 -0
  49. pusht_dataset/goal/goal_0033.npz +3 -0
  50. pusht_dataset/goal/goal_0034.npz +3 -0
pusht_dataset/README.md ADDED
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+ # PushT Dataset
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+
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+ Video + action data from the [gym-pusht](https://github.com/huggingface/gym-pusht) environment. Three splits:
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+
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+ | Split | Episodes | Avg steps/ep | Hours | Description |
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+ |-------|----------|-------------|-------|-------------|
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+ | `smooth/` | ~38,900 | 300 | ~324 hrs | Random smooth movement (Ornstein-Uhlenbeck process) |
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+ | `goal/` | ~8,700 | 298 | ~73 hrs | Heuristic goal-directed policy (keypoint matching) |
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+ | `expert/` | ~11,500 | 228 | ~73 hrs | Pretrained diffusion policy, ~74% success rate |
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+
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+ ## File format
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+
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+ Each `.npz` file contains multiple episodes. Load with:
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+
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+ ```python
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+ import numpy as np
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+
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+ data = np.load("smooth/smooth_0000_00.npz", allow_pickle=True)
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+ n = int(data["num_trajectories"]) # number of episodes in this file
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+
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+ for i in range(n):
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+ frames = data[f"frames_{i}"] # (T+1, 96, 96, 3) uint8 — RGB pixel observations
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+ actions = data[f"actions_{i}"] # (T, 2) float32 — agent target position [x, y] in [0, 512]
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+ rewards = data[f"rewards_{i}"] # (T,) float32 — coverage ratio, 1.0 = solved
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+ policy = str(data[f"policy_{i}"]) # "smooth", "goal", or "expert"
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+ ```
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+
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+ - `frames` has one more entry than `actions` (initial frame before first action)
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+ - `frames[t]` is the observation *before* `actions[t]` is taken
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+ - `frames[t+1]` is the observation *after* `actions[t]` is taken
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+ - The environment runs at 10 Hz (0.1s per step)
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+ - An episode is "solved" when `rewards[t] >= 0.95` (the T-block covers >95% of the goal)
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