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nikitapatel
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robotics-sensor-fusion

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robotics
sensor-fusion
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Dataset Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    AttributeError
Message:      'str' object has no attribute 'items'
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
                  config_names = get_dataset_config_names(
                      path=dataset,
                      token=hf_token,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                      path,
                  ...<4 lines>...
                      **download_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1182, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1157, in dataset_module_factory
                  ).get_module()
                    ~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 692, in get_module
                  config_name: DatasetInfo.from_dict(dataset_info_dict)
                               ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/info.py", line 284, in from_dict
                  return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
                                                 ^^^^^^^^^^^^^^^^^^^^^^^
              AttributeError: 'str' object has no attribute 'items'

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Notes on Robotics Sensor Fusion data

Overview

A documented Robotics data-preparation workflow for Sensor Fusion records. The bundled rows demonstrate the schema and validation path rather than pretending to be a full training corpus.

Changelog

  • 2026-10-20 — initial upload
  • 2026-10-21 — config and docs sync

Processing choices

  • Storage format: tfrecord
  • Preprocessing: aggressive
  • Augmentation: randaugment
  • Split strategy: stratified 90 10
  • Sampling: weighted
  • Quality checks: adaptive
  • Labeling: manual

Roadmap

Next steps, in no particular order: a cleaner entry point, input-shape docs, and a small evaluation script if time permits.

Risks and limitations

External sources may be unrepresentative, mis-licensed, or contain sensitive fields. Source review, privacy checks and bias analysis are the user's responsibility before training or redistribution.

Validation checklist

  • check source licenses before redistribution
  • scan for duplicates across splits
  • inspect missing values and label balance
  • watch for modality-specific corruption

Included material

  • prepare.py — loading, cleaning, and split preparation code.
  • dataset_infos.json — schema and split metadata.
  • metadata_sample.jsonl — small, human-readable records for checking the schema.
  • README.md — data card and usage notes.

Files

  • prepare.py — primary artifact
  • README.md — this documentation
  • dataset_infos.json — schema metadata
  • metadata_sample.jsonl — schema sample
  • examples/sample_usage.py — usage sketch

License

Distributed under the cc-by-4.0 license. External datasets keep their own terms — check them separately.
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