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list | state
list | timestamp
float32 | frame_index
int64 | episode_index
int64 | index
int64 | task_index
int64 |
|---|---|---|---|---|---|---|
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] | 3.233333 | 97 | 0 | 97 | 0 |
[
-11.963540077209473,
-1.6891891956329346,
38.795833587646484,
51.299827575683594,
-6.284224033355713,
21.792762756347656
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[
-13.08237075805664,
-1.416917085647583,
40.045352935791016,
51.787269592285156,
-6.153443336486816,
22.447446823120117
] | 3.266667 | 98 | 0 | 98 | 0 |
[
-11.963540077209473,
-1.7736486196517944,
38.795833587646484,
51.299827575683594,
-6.284224033355713,
21.792762756347656
] |
[
-13.08237075805664,
-1.416917085647583,
40.045352935791016,
51.787269592285156,
-6.153443336486816,
22.447446823120117
] | 3.3 | 99 | 0 | 99 | 0 |
End of preview. Expand
in Data Studio
SO101 Merged Strawberries & Tic-Tac-Toe Dataset
Dataset Description
This dataset contains robot manipulation episodes combining two distinct tasks using SO101 robot. The dataset includes 27 episodes of robot manipulation with multi-view camera observations.
Tasks
Task 0: Pick up strawberries and put them in a tray Task 1: Pick up an X piece and place it on the tic-tac-toe board to complete the winning line
Robot: SO101 Follower robot with 6 degrees of freedom
Dataset Structure
This dataset follows the LeRobot format and contains:
- Data: Parquet files containing robot states, actions, and metadata
- Videos: MP4 files with camera observations (front and wrist views)
- Metadata: JSON files with episode information and statistics
Task Distribution
| Task | Description | Episodes | Frames |
|---|---|---|---|
| 0 | Pick up the strawberries and put it in the tray | 19 | 32641 |
| 1 | Pick up an X piece and place it on the tic-tac-toe board to complete the winning line | 8 | 13734 |
Features
Action: 6-dimensional robot joint positions
shoulder_pan.pos: Shoulder pan joint positionshoulder_lift.pos: Shoulder lift joint positionelbow_flex.pos: Elbow flex joint positionwrist_flex.pos: Wrist flex joint positionwrist_roll.pos: Wrist roll joint positiongripper.pos: Gripper position
Observation State: 6-dimensional robot joint states (same as action)
Images: Multi-view camera observations
observation.images.front: Front camera view (256x256x3 RGB)observation.images.wrist: Wrist camera view (256x256x3 RGB)
Metadata:
timestamp: Frame timestamps in secondsframe_index: Frame index within episodeepisode_index: Episode indextask_index: Task index (0 for strawberry picking, 1 for tic-tac-toe)
Usage
Using LeRobot
from lerobot import load_dataset
# Load the dataset
dataset = load_dataset("kinam0252/merged_strawberries_tictactoe")
# Access episodes by task
strawberry_episodes = dataset.filter(lambda x: x["task_index"] == 0)
tic_tac_toe_episodes = dataset.filter(lambda x: x["task_index"] == 1)
# Access episodes
for episode in dataset:
print(f"Episode {episode['episode_index']}: {len(episode)} frames")
print(f"Task: {episode['task_index']}")
print(f"Actions shape: {episode['action'].shape}")
print(f"Images keys: {list(episode['observation.images'].keys())}")
Direct Loading
import pandas as pd
import json
# Load episode data
df = pd.read_parquet("data/chunk-000/episode_000000.parquet")
# Load metadata
with open("meta/info.json", "r") as f:
info = json.load(f)
# Access robot actions and states
actions = df["action"].values
states = df["observation.state"].values
# Filter by task
strawberry_data = df[df["task_index"] == 0]
tic_tac_toe_data = df[df["task_index"] == 1]
Dataset Statistics
- Total Episodes: 27
- Total Frames: 46375
- Total Tasks: 2
- FPS: 30
- Average Episode Length:
1717 frames (57 seconds) - Robot DOF: 6 (shoulder_pan, shoulder_lift, elbow_flex, wrist_flex, wrist_roll, gripper)
- Video Resolution: 256x256x3 RGB
- Video Codec: AV1
Citation
If you use this dataset, please cite:
@dataset{merged_strawberries_tictactoe_2024,
title={SO101 Merged Strawberries & Tic-Tac-Toe Dataset},
author={Your Name},
year={2024},
publisher={Hugging Face},
url={https://huggingface.co/datasets/kinam0252/merged_strawberries_tictactoe}
}
License
This dataset is licensed under the MIT license.
Acknowledgments
- Built using the LeRobot framework
- Robot demonstrations performed with SO101 robot arm
- Video encoding optimized with AV1 codec for efficient storage
- Merged from two distinct manipulation tasks for multi-task learning
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