---
license: cc-by-4.0
pretty_name: KlaskTron
source_datasets:
  - original
size_categories:
  - 1M<n<10M
tags:
  - imitation-learning
  - reinforcement-learning
  - robotics
  - control
  - simulation
  - klask
  - parquet
  - pickle
  - tabular
  - datasets
  - mlcroissant
task_categories:
  - robotics
  - reinforcement-learning
configs:
  - config_name: human
    data_files:
      - split: train
        path: "parquet/human/*.parquet"
  - config_name: human_augmented
    data_files:
      - split: train
        path: "parquet/human_augmented/*.parquet"
  - config_name: synthetic
    default: true
    data_files:
      - split: train
        path: "parquet/synthetic/*.parquet"
---

# KlaskTron

KlaskTron is a Klask imitation-learning dataset with three tiers: real human play, axis-mirror-augmented human data, and large-scale synthetic rollouts.

| Tier | Episodes | Steps | Source |
| --- | ---: | ---: | --- |
| `human` | 111 | 80,591 | reconstructed from recorded human play |
| `human_augmented` | 444 | 322,364 | axis-mirror augmentation of the human tier (none, x, y, xy) |
| `synthetic` | 34,649 | 5,000,000 | generated in simulation from an expert-like policy |

Total: 35,204 episodes and 5,402,955 action steps.

## Loading Parquet

```python
from datasets import load_dataset

human = load_dataset("KlaskLab/klasktron-il-benchmark", "human", split="train")
human_augmented = load_dataset("KlaskLab/klasktron-il-benchmark", "human_augmented", split="train")
synthetic = load_dataset("KlaskLab/klasktron-il-benchmark", "synthetic", split="train")
```

Each Parquet row is one transition:

| Column | Meaning |
| --- | --- |
| `tier` | `human`, `human_augmented`, or `synthetic` |
| `episode_id` | tier-local episode id |
| `source_file` | original pickle file |
| `episode_in_file` | episode index inside that pickle |
| `step` | transition index inside the episode |
| `obs` | state at time `t`, shape `[12]` |
| `act` | action at time `t`, shape `[4]` |
| `next_obs` | state at time `t + 1`, shape `[12]` |
| `rew` | all-zero placeholder |
| `terminal` | true only on the final transition of a terminal episode |

Observation layout:

```text
[ball_x, ball_y,
 ball_vx, ball_vy,
 peg1_x, peg1_y,
 peg2_x, peg2_y,
 peg1_vx, peg1_vy,
 peg2_vx, peg2_vy]
```

Action layout:

```text
[peg1_vx, peg1_vy, peg2_vx, peg2_vy]
```

## Loading Pickles

Raw pickle files live under:

```text
human/trajectories/
human_augmented/trajectories/
synthetic/trajectories/
```

Episode fields:

- `obs`: float32 array, shape `[T + 1, 12]`
- `acts`: float32 array, shape `[T, 4]`
- `rews`: float32 array, shape `[T]` where present
- `infos`: optional per-step metadata
- `terminal`: whether the rollout ends naturally

Raw container types:

| Tier | Container |
| --- | --- |
| `human` | `imitation.data.types.TrajectoryWithRew` |
| `human_augmented` | `imitation.data.types.Trajectory` |
| `synthetic` | plain Python `dict` |

The human and human_augmented pickles need a compatible Python environment with `imitation` available. The synthetic pickles only use plain dictionaries and NumPy arrays. As usual, only unpickle data you trust.

## Rewards

There is no meaningful per-step reward signal. `rew` / `rews` is an all-zero placeholder and should not be used for offline-RL returns, filtering, or scoring.

## Notes

- The human tier comes from 111 recorded human Klask trajectories that were reestimated into simulation coordinates.
- The human_augmented tier extends the human tier 4x by applying axis-mirror augmentation (none, x, y, xy) to each trajectory.
- The synthetic tier reflects simulator and expert-policy assumptions.

### Why Both Formats Exist

The pickle files are the original episode objects used by imitation-learning code that expects `imitation.data.types.Trajectory` / `TrajectoryWithRew` objects. The mirrored Parquet folder exposes the same trajectories as a safer, easier-to-inspect transition table for Hugging Face Datasets, pandas, Polars, DuckDB, PyArrow, browser previews, and Croissant-compatible tooling.

## License

Creative Commons Attribution 4.0 International License (CC-BY-4.0).

## Citation

```bibtex
@article{klasktron2026,
  title={KlaskTron: A Contact-Rich, Adversarial Benchmark for Imitation Learning},
  author={Anonymous},
  year={2026},
  note={Under review at NeurIPS 2026}
}
```