new_GtoR TsFile

This dataset is a TsFile conversion of FeiYjf/new_GtoR, a LeRobot SO-100 robotics dataset. The source dataset card states that it was created using LeRobot.

Modalities: Time-series. The original camera videos are not uploaded here; they remain available in the source dataset under videos/.

Source Dataset

• Source: FeiYjf/new_GtoR
• License: apache-2.0
• Source tags: LeRobot, tutorial
• Codebase version: v2.0
• Robot type: so100
• Split: train 0:378
• Scale: 378 episodes, 126,369 frames, 1 task, 1 data chunk, 756 videos
• Sampling rate: 30 fps
• Task: new_GtoR

Converted Files

• TsFile path: data/new_gtor.tsfile
• Table name: new_gtor
• Row count: 126,369
• Time precision: milliseconds
• Metadata: meta/ is mirrored from the source dataset, with meta/info.json updated to describe the converted TsFile artifact and video policy.

Schema

Time is generated as round(timestamp * 1000) in milliseconds. Time restarts within each episode, and episode_index plus task_index identify the series.

TAG columns:

• episode_index
• task_index

FIELD columns:

• frame_index
• sample_index converted from the source index
• action_0 to action_5
• observation_state_0 to observation_state_5

The action and observation vectors use these source element names in order:

• main_shoulder_pan
• main_shoulder_lift
• main_elbow_flex
• main_wrist_flex
• main_wrist_roll
• main_gripper

Conversion Notes

• All 378 train episodes are stored in a single TsFile using the TsFile table model, with episode_index and task_index as TAG columns.
• The source timestamp column is not retained as a FIELD because it is the source for Time and equals Time / 1000 seconds after conversion.
• The source index column is renamed to sample_index.
• action[6] is flattened to scalar FLOAT fields action_0 to action_5.
• observation.state[6] is flattened to scalar FLOAT fields observation_state_0 to observation_state_5.
• Video features are omitted from this repository: observation.images.left and observation.images.right. Use the original dataset videos linked above for frame-aligned visual data.

Minimal Read Example

from tsfile import TsFileReader

path = "data/new_gtor.tsfile"
reader = TsFileReader(path)

columns = [
    "episode_index",
    "task_index",
    "frame_index",
    "sample_index",
    "action_0",
    "observation_state_0",
]

with reader.query_table("new_gtor", columns, batch_size=1024) as result:
    batch = result.read_arrow_batch()
    if batch is not None:
        df = batch.to_pandas()
        print(df.head())