fractal20220817_data_lerobot
Large LeRobot dataset with 87212 Google robot episodes across 599 tasks.
Open dataset Updated 2026-06-18
What is fractal20220817_data_lerobot?
A robotics dataset generated with LeRobot v2.0, containing 87212 episodes and 3786400 frames from google_robot across 599 tasks at 3 FPS.
Useful for training and evaluating imitation learning or robot policy models on manipulation tasks.
What you can build with fractal20220817_data_lerobot
Train imitation learning policies
Use the 87k robot episodes to train visuomotor policies that map camera observations to actions for the 599 covered tasks.
Benchmark multi-task robotics models
Evaluate generalization across 599 tasks using the single train split of 3.7M frames recorded from a Google robot.
Develop video-based task planners
Leverage the 87k provided videos to build models that predict task sequences or detect task completion from low-FPS footage.
Load fractal20220817_data_lerobot
Python
from datasets import load_dataset
ds = load_dataset("IPEC-COMMUNITY/fractal20220817_data_lerobot")- 1pip install datasets
- 2from datasets import load_dataset
- 3ds = load_dataset('IPEC-COMMUNITY/fractal20220817_data_lerobot')
- 4Access episodes via the 'train' split and Parquet columns for frames/actions
- 5Convert frames to video or tensor format for LeRobot training pipelines
fractal20220817_data_lerobot: pros & cons
Pros
- +Very large scale: 87k episodes and 3.7M frames
- +Covers 599 distinct tasks from a real Google robot
- +Ready-to-use Parquet format with videos included
- +Created with LeRobot v2.0 for direct compatibility
Cons
- –Only a single train split; no validation/test sets
- –Recorded at low 3 FPS, limiting fine-grained motion capture
- –Hardware-specific to Google robot platform
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Frequently asked questions
A large robotics dataset of 87,212 episodes (3.7M frames) from a Google robot covering 599 tasks, stored in Parquet with videos.
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