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update: complete dataset card

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  1. README.md +29 -25
README.md CHANGED
@@ -1,29 +1,30 @@
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- yaml
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  dataset_info:
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- license: other
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- license_name: chingmu-terms
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- license_link: LICENSE
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- language: ["en", "zh"]
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- pretty_name: "ChingMu Robot Motion Dataset"
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- tags:
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- motion-capture
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- humanoid-robotics
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- imitation-learning
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- optical-mocap
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- bvh
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- dexterous-hands
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- whole-body-control
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- size_categories: 1M<n
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- configs:
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- config_name: metadata
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- default: true
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- data_files:
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- split: train
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- path: "metadata/index.csv"
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- config_name: samples
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- data_files:
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- split: train
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- path: "samples/*/"
 
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  # ChingMu 1000-Hour Embodied Motion Dataset
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  ### 青瞳1000小时具身智能动作数据集
@@ -95,12 +96,15 @@ All motion takes are indexed in `metadata/index.csv`. The key **filter columns**
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  ## Dataset Structure
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  After requesting access and downloading:
 
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  ---
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  ## Quick Start — Browse & Download
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  ### Step 1: Load the index (NO need to download big files yet)
 
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  ### Step 2: Filter by your tags
 
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  ### Step 3: Download ONLY the files you selected
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  > ⚠️ **Don't** `git clone` the whole repo without sparse checkout — the full dataset is very large.
 
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+ ---
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  dataset_info:
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+ license: other
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+ license_name: chingmu-terms
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+ license_link: LICENSE
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+ language: ["en", "zh"]
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+ pretty_name: "ChingMu Robot Motion Dataset"
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+ tags:
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+ - motion-capture
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+ - humanoid-robotics
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+ - imitation-learning
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+ - optical-mocap
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+ - bvh
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+ - dexterous-hands
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+ - whole-body-control
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+ size_categories: 1M<n
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+ configs:
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+ - config_name: metadata
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+ default: true
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+ data_files:
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+ - split: train
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+ path: "metadata/index.csv"
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+ - config_name: samples
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+ data_files:
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+ - split: train
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+ path: "samples/**/*"
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+ ---
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  # ChingMu 1000-Hour Embodied Motion Dataset
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  ### 青瞳1000小时具身智能动作数据集
 
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  ## Dataset Structure
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  After requesting access and downloading:
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+
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  ---
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  ## Quick Start — Browse & Download
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  ### Step 1: Load the index (NO need to download big files yet)
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+
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  ### Step 2: Filter by your tags
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+
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  ### Step 3: Download ONLY the files you selected
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  > ⚠️ **Don't** `git clone` the whole repo without sparse checkout — the full dataset is very large.