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robotic-manipulation-dataset

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Check out the documentation for more information.

Robotic Manipulation Dataset

This dataset contains multimodal data collected from real-world robotic manipulation tasks using a D435 depth camera and additional sensors. It is intended to support research in robot learning, perception, manipulation planning, and imitation learning.

Usage Examlpes

For usage examples, evaluation metrics, and fine-tuning code on this dataset, please refer to:

Robotic Action Frame Prediction

📁 Dataset Structure

The dataset is organized into three task categories, each stored in a compressed .tar.gz archive:

  • block_hammer_beat_sf50_D435_pkl.tar.gz: hammering task with block object
  • block_handover_sf50_D435_pkl.tar.gz: block handover task between robots or human-robot
  • blocks_stack_easy_sf50_D435_pkl.tar.gz: simple block stacking task

After decompression, each archive contains multiple episodes (folders), where each episode stores sensor recordings and metadata.

Each Episode Contains:

  • rgb/ — RGB images captured by the D435 camera
  • depth/ — depth images
  • meta.pkl — metadata including timestamp, camera pose, robot state, etc.
  • (optional) other .pkl files recording joint states, gripper states, and actions

📥 Download

You can download and extract the full dataset using the following Bash script:

mkdir -p original_data
wget https://huggingface.co/datasets/yutengz/robotic-manipulation-dataset/resolve/main/block_hammer_beat_sf50_D435_pkl.tar.gz
wget https://huggingface.co/datasets/yutengz/robotic-manipulation-dataset/resolve/main/block_handover_sf50_D435_pkl.tar.gz
wget https://huggingface.co/datasets/yutengz/robotic-manipulation-dataset/resolve/main/blocks_stack_easy_sf50_D435_pkl.tar.gz

tar -xzf block_hammer_beat_sf50_D435_pkl.tar.gz -C original_data/
tar -xzf block_handover_sf50_D435_pkl.tar.gz -C original_data/
tar -xzf blocks_stack_easy_sf50_D435_pkl.tar.gz -C original_data/

rm *.tar.gz
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Size of downloaded dataset files:
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Size of the auto-converted Parquet files:
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Number of rows:
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