Create README.md

README.md

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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.