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--- |
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license: apache-2.0 |
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task_categories: |
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- reinforcement-learning |
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- other |
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language: |
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- code |
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tags: |
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- minecraft |
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- expert-demonstrations |
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- skill-segmentation |
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- action-segmentation |
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- object-centric |
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pretty_name: Minecraft Skill Segmentation Dataset |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Dataset Card for Minecraft Expert Skill Data |
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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. |
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## Dataset Details |
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### Dataset Description |
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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. |
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- **Curated by:** [dami2106] |
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- **License:** Apache 2.0 |
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- **Language(s) (NLP):** Not applicable (code/visual) |
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### Dataset Sources |
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- **Repository:** https://github.com/dami2106/Minecraft-Skill-Data |
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## Uses |
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### Direct Use |
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This dataset is designed for use in: |
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- Training models to segment long-horizon behaviors into reusable skills. |
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- Evaluating action segmentation or hierarchical RL approaches. |
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- Studying object-centric or spatially grounded RL methods. |
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- Pretraining representations from visual expert data. |
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### Out-of-Scope Use |
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- Language-based tasks (no natural language data is included). |
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- Real-world robotics (simulation-only data). |
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- Tasks requiring raw image pixels if they are not included in your setup. |
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## Dataset Structure |
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Each data file includes: |
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- A sequence of states (e.g., pixel POV observations, PCA features). |
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- Skill labels marking where each skill begins and ends. |
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Example structure: |
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```json |
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{ |
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"pixel_obs": [...], // Raw visual observations (e.g., RGB frames) |
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"pca_features": [...], // Compressed feature vectors (e.g., from CNN or ResNet) |
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"groundTruth": [...], // Ground-truth skill segmentation labels |
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"mapping": { // Mapping metadata for skill ID -> groundTruth label |
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"0": "chop_tree", |
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"1": "craft_table", |
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"2": "mine_stone", |
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... |
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} |
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} |
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``` |
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## Dataset Creation |
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### Curation Rationale |
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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. |
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### Source Data |
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#### Data Collection and Processing |
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- Expert trajectories were generated using a scripted or trained policy within the Minecraft simulation. |
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- Skill labels were added based on environment signals (e.g., changes to inventory, task completions, block state transitions) and verified using heuristics. |
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#### Who are the source data producers? |
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The data was generated programmatically in the Minecraft simulation environment by expert agents using scripted or learned behavior policies. |
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### Annotations |
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#### Annotation process |
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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. |
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#### Who are the annotators? |
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Automated rule-based annotation systems with developer oversight during dataset development. |
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## Bias, Risks, and Limitations |
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- The dataset is derived from simulation, so its findings may not generalize to real-world robotics or broader RL environments. |
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- Skill definitions depend on domain-specific heuristics, which may not reflect all valid strategies. |
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- Expert strategies may be biased toward specific pathways (e.g., speedrunning logic). |
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### Recommendations |
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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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