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---
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.
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