Datasets:
YAML Metadata Warning:The task_categories "planning" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
GSI
GSI Dataset (Semantic Platform): Multi-robot planning dataset with 1 scenarios, 200000 goals, 200000 tasks, and 0 prompts
Dataset Description
This dataset contains a complete multi-robot planning dataset with scenarios, goals, tasks, and prompts.
Dataset Structure
semantic/
├── tasks/ # Task definitions
│ └── {type}/
│ └── tasks.jsonl
├── scenarios/ # Scenario configurations
│ └── {type}/
│ └── {scenario_id}/
│ ├── scene_graph.json
│ ├── plans.json
│ └── scene.png
├── goals/ # Goal definitions
│ └── {type}/
│ └── goals.jsonl
└── prompts/ # Generated prompts (deduplicated)
└── {type}/
├── prompts.jsonl
├── pool_*.json
└── config.json
Dataset Statistics
- Types: cybertown
- Scenarios: 1
- Goals: 200000
- Tasks: 200000
- Prompts: 0
Usage
Loading the Dataset
from pathlib import Path
import json
# Load tasks
tasks_file = Path('tasks/cybertown/tasks.jsonl')
with open(tasks_file, 'r') as f:
tasks = [json.loads(line) for line in f]
# Load scenarios
scenarios_dir = Path('scenarios/cybertown')
scenarios = {}
for scenario_dir in scenarios_dir.iterdir():
if scenario_dir.is_dir():
with open(scenario_dir / 'scene_graph.json', 'r') as f:
scenarios[scenario_dir.name] = json.load(f)
# Load goals
goals_file = Path('goals/cybertown/goals.jsonl')
with open(goals_file, 'r') as f:
goals = [json.loads(line) for line in f]
# Load prompts (deduplicated format)
prompts_dir = Path('prompts/cybertown')
# Load config
with open(prompts_dir / 'config.json', 'r') as f:
config = json.load(f)
# Load text pools
pools = {}
for pool_file in prompts_dir.glob('pool_*.json'):
pool_name = pool_file.stem.replace('pool_', '')
with open(pool_file, 'r') as f:
pools[pool_name] = json.load(f)
# Load main prompts data
with open(prompts_dir / 'prompts.jsonl', 'r') as f:
for line in f:
record = json.loads(line)
# Reconstruct full prompt using indices and pools
# record contains indices like skill_set_idx, env_desc_idx, etc.
...
Prompt Configuration
Citation
If you use this dataset, please cite:
@dataset{gsi,
title = {GSI},
author = {Windy Lab},
year = {2025},
license = {MIT},
}
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