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Datasets:
THULab
/
nvidia_workspace

Tasks:
Robotics
Modalities:
Time-series
Size:
10K<n<100K
Tags:
LeRobot
tsfile
timeseries
robotics
format:tsfile
License:
Dataset card Files
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Episodes Preview SO-101 Visualizer
27 episodes · 30 fps

NVIDIA WORKSPACE (TsFile)

Apache TsFile version of tremmelnicholas/NVIDIA_WORKSPACE.

Overview

This LeRobot v3.0 dataset records an SO-101 follower robot performing the task "Pick up the vial and place it in the rack."

  • Scale: 27 episodes, 19,913 frames, one task, and one train split.
  • Sampling rate: 30 Hz.
  • Modalities: Time-series. The source dataset also provides video.
  • Source cameras: ego and external Intel RealSense D455 streams at 640 x 480, encoded as AV1 video at 30 fps.
  • Artifact: one TsFile, data/nvidia_workspace_train.tsfile.

TsFile schema

  • Time (INT64, milliseconds): round(timestamp * 1000), restarting for each episode.
  • episode_index, task_index (TAG): episode and task dimensions. Use the TAG columns to select one trajectory, for example episode_index=0.
  • frame_index, sample_index (FIELD, INT64): the frame position within an episode and the renamed global source index.
  • observation_state_0..observation_state_5 (FIELD, FLOAT): shoulder pan, shoulder lift, elbow flex, wrist flex, wrist roll, and gripper positions.
  • action_0..action_5 (FIELD, FLOAT): commands for the same six robot axes.

The source observation.state[6] and action[6] vectors are flattened without discarding elements. The source timestamp field is represented by Time and is not duplicated as a FIELD. Source index is renamed to sample_index.

The two video features, observation.images.ego and observation.images.external_D455, are not uploaded here. Their MP4 files remain available in the original videos directory. The mirrored meta/info.json records the flattened columns, time mapping, omitted video features, and row count.

Usage 

from tsfile import TsFileReader

reader = TsFileReader("data/nvidia_workspace_train.tsfile")
schemas = reader.get_all_table_schemas()
print(schemas)
reader.close()

All episodes share the nvidia_workspace_train table. Query by episode_index and task_index to isolate a trajectory.

Source and license

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