README.md

---
license: apache-2.0
task_categories:
- reinforcement-learning
- other
language:
- code
tags:
- minecraft
- expert-demonstrations
- skill-segmentation
- action-segmentation
- object-centric
pretty_name: Minecraft Skill Segmentation Dataset
size_categories:
- 1K<n<10K
---
# Dataset Card for Minecraft Expert Skill Data
This dataset consists of expert demonstration trajectories from a Minecraft simulation environment. Each trajectory includes ground-truth skill segmentation annotations, enabling research into action segmentation, skill discovery, imitation learning, and reinforcement learning with temporally-structured data.

## Dataset Details

### Dataset Description

The **Minecraft Skill Segmentation Dataset** contains gameplay trajectories from an expert policy in the Minecraft environment. Each trajectory is labeled with ground-truth skill boundaries and skill identifiers, allowing users to train and evaluate models for temporal segmentation, behavior cloning, and skill-based representation learning.

- **Curated by:** [dami2106]
- **License:** Apache 2.0
- **Language(s) (NLP):** Not applicable (code/visual)

### Dataset Sources

- **Repository:** https://github.com/dami2106/Minecraft-Skill-Data

## Uses

### Direct Use

This dataset is designed for use in:
- Training models to segment long-horizon behaviors into reusable skills.
- Evaluating action segmentation or hierarchical RL approaches.
- Studying object-centric or spatially grounded RL methods.
- Pretraining representations from visual expert data.

### Out-of-Scope Use

- Language-based tasks (no natural language data is included).
- Real-world robotics (simulation-only data).
- Tasks requiring raw image pixels if they are not included in your setup.

## Dataset Structure

Each data file includes:
- A sequence of states (e.g., pixel POV observations, PCA features).
- Skill labels marking where each skill begins and ends.

Example structure:
```json
{
  "pixel_obs": [...],         // Raw visual observations (e.g., RGB frames)
  "pca_features": [...],      // Compressed feature vectors (e.g., from CNN or ResNet)
  "groundTruth": [...],       // Ground-truth skill segmentation labels
  "mapping": {                // Mapping metadata for skill ID -> groundTruth label
    "0": "chop_tree",
    "1": "craft_table",
    "2": "mine_stone",
    ...
  }
}
```

## Dataset Creation

### Curation Rationale

This dataset was created to support research in skill discovery and temporal abstraction in complex, open-ended environments like Minecraft. The environment supports high-level goals and diverse interactions, making it suitable for testing generalizable skills.

### Source Data

#### Data Collection and Processing

- Expert trajectories were generated using a scripted or trained policy within the Minecraft simulation.
- Skill labels were added based on environment signals (e.g., changes to inventory, task completions, block state transitions) and verified using heuristics.

#### Who are the source data producers?

The data was generated programmatically in the Minecraft simulation environment by expert agents using scripted or learned behavior policies.

### Annotations

#### Annotation process

Skill annotations were derived from internal game state events and heuristics related to player intent and task segmentation. Manual inspection was performed to ensure consistency across trajectories.

#### Who are the annotators?

Automated rule-based annotation systems with developer oversight during dataset development.

## Bias, Risks, and Limitations

- The dataset is derived from simulation, so its findings may not generalize to real-world robotics or broader RL environments.
- Skill definitions depend on domain-specific heuristics, which may not reflect all valid strategies.
- Expert strategies may be biased toward specific pathways (e.g., speedrunning logic).

### Recommendations

Researchers should evaluate the robustness of learned skills across diverse environments and initial conditions. Segmentations reflect task approximations and should be interpreted within the scope of the simulation constraints.