Datasets:

Sraghvi
/

CSV_Robot_Dataset_Clean

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 2 was different: 
episode_index: int64
episode_length: int64
episode_path: string
vs
codebase_version: string
robot_type: string
total_episodes: int64
total_frames: int64
total_tasks: int64
total_videos: int64
total_chunks: int64
chunks_size: int64
fps: double
splits: struct<train: string>
data_path: string
video_path: string
features: struct<observation.state: struct<dtype: string, shape: list<item: int64>, names: list<item: string>>, action: struct<dtype: string, shape: list<item: int64>, names: list<item: string>>, observation.images.camera: struct<dtype: string, shape: list<item: int64>, names: list<item: string>, info: struct<video.height: int64, video.width: int64, video.codec: string, video.pix_fmt: string, video.is_depth_map: bool, video.fps: double, video.channels: int64, has_audio: bool>>, timestamp: struct<dtype: string, shape: list<item: int64>, names: null>, frame_index: struct<dtype: string, shape: list<item: int64>, names: null>, episode_index: struct<dtype: string, shape: list<item: int64>, names: null>, index: struct<dtype: string, shape: list<item: int64>, names: null>, task_index: struct<dtype: string, shape: list<item: int64>, names: null>>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 531, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 2 was different: 
              episode_index: int64
              episode_length: int64
              episode_path: string
              vs
              codebase_version: string
              robot_type: string
              total_episodes: int64
              total_frames: int64
              total_tasks: int64
              total_videos: int64
              total_chunks: int64
              chunks_size: int64
              fps: double
              splits: struct<train: string>
              data_path: string
              video_path: string
              features: struct<observation.state: struct<dtype: string, shape: list<item: int64>, names: list<item: string>>, action: struct<dtype: string, shape: list<item: int64>, names: list<item: string>>, observation.images.camera: struct<dtype: string, shape: list<item: int64>, names: list<item: string>, info: struct<video.height: int64, video.width: int64, video.codec: string, video.pix_fmt: string, video.is_depth_map: bool, video.fps: double, video.channels: int64, has_audio: bool>>, timestamp: struct<dtype: string, shape: list<item: int64>, names: null>, frame_index: struct<dtype: string, shape: list<item: int64>, names: null>, episode_index: struct<dtype: string, shape: list<item: int64>, names: null>, index: struct<dtype: string, shape: list<item: int64>, names: null>, task_index: struct<dtype: string, shape: list<item: int64>, names: null>>

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CSV_Robot_Dataset

Robot manipulation dataset with 2 episodes.

Dataset Structure

Episodes: 2 episodes of robot manipulation
Cameras: 3 camera views (base, arm1, arm2)
Joints: 34 joint states
Format: LeRobot v2.1

Usage

from lerobot.common.datasets import LeRobotDataset

dataset = LeRobotDataset("Sraghvi/shrugmaster-robot-dataset")

Files

data/chunk-000/: Parquet files with episode data
videos/chunk-000/: MP4 video files for each camera (contains all image frames)
meta/: Metadata files (info.json, episodes.jsonl, etc.)

Dataset Details

Total Episodes: 2
Total Frames: 2832
Average Episode Length: 1416 frames
FPS: 8.9 (calculated from real data)
Robot Type: Bimanual arm with 34 joints
Camera Resolution: 1280x720
Video Codec: H.264

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