The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 84, in _split_generators
raise ValueError(
...<2 lines>...
)
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
ReFrame Handheld Demonstration Dataset
ReFrame-Handheld provides real handheld operation recordings for studying how demonstrations can be reused across camera configurations. It contains 1,651 independent recordings across four manipulation tasks, with camera images, per-frame timestamps, raw SLAM poses, gripper readings and camera calibration.
The data supports inspecting viewpoint changes, testing geometric reprojection and comparing transformed images with available real reference-camera observations. Videos and structured numerical files can be read without a ROS environment.
1,651 recordings · 4 tasks · approximately 303 recorded minutes · 67 GB of episode data
Observations
- 1,451 recordings contain main RGB, left and right grayscale cameras, and a real USB reference-camera stream.
- 200 recordings contain main RGB without auxiliary images.
- All recordings retain SLAM pose and gripper samples. Each episode references its applicable calibration profile.
The recordings use the same brand and model of capture device with three camera-extrinsic configurations. The dataset overview describes the observation types, statistics and counting rules.
Tasks
| Task | Independent recordings | Approximate episode download size |
|---|---|---|
| Carrot picking and placing | 696 | 34.49 GB |
| Button pressing | 500 | 19.69 GB |
| Drawer opening | 255 | 9.01 GB |
| Cup hanging | 200 | 4.05 GB |
| Total | 1,651 | 67.25 GB |
Sizes refer to episode files, excluding the optional starter ZIP and shared documentation. Recorded duration includes recording margins and is counted once per operation, not once per camera.
Download and try
Start with 17 complete examples — approximately 788 MB, covering all retained acquisition batches and all four tasks. The ZIP includes calibration, reading tools and instructions, and works independently after extraction.
All episode data is distributed as TAR shards, grouped by task and acquisition batch. Each shard contains complete episodes with their original files and paths. The download tool downloads the required shards, verifies SHA256 and extracts the selected episodes with their calibration. Selecting one episode downloads its containing shard; see the archive index for exact sizes.
Open an MP4 directly to inspect a recording. For Python access to images, timestamps, poses, gripper readings and calibration, follow the quickstart.
Documentation
- Dataset overview and statistics
- File format and reading interfaces
- Timestamp semantics and optional frame matching
- Camera calibration
- Selection, task-outcome labels and known limitations
The dataset preserves original operation sequences and quality annotations. See the linked documentation for timestamp interpretation, outcome labels and calibration applicability.
License and citation
[TO FILL: confirmed dataset license]
[TO FILL: authors and paper citation]
[TO FILL: paper, code repository and contact]
This dataset accompanies ReFrame: Reusing Handheld Demonstrations Across Wrist-Camera Configurations. Licensing and paper citation details are pending owner confirmation.
Additional calibration candidates
Brown5 and fisheye4 intrinsic refits are available for the Alice and David USB cameras. They are optional candidates; episode references continue to use the historical calibration profiles. See the 2026-10-29 calibration update.
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