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train

The dataset viewer is not available for this split.

Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Dataset 'human_prior' has length 4096 but expected 3
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 80, in _generate_tables
                  num_rows = _check_dataset_lengths(h5, self.info.features)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 359, in _check_dataset_lengths
                  raise ValueError(f"Dataset '{path}' has length {dset.shape[0]} but expected {num_rows}")
              ValueError: Dataset 'human_prior' has length 4096 but expected 3

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UCBProject / ProcessedData

Phase 1A outputs of the Affordance2Grasp pipeline — used by downstream phases:

Phase 2 (training the main method's PointNet++ contact-prediction net) reads training_fp/{dataset}/{object}.hdf5.
Phase 3 (inference, grasp sampling, sim execution, Sim2Real deployment) reads per-object affordance priors from human_prior_fp/{object}.hdf5.

The upstream object meshes (SAM3D reconstructions) live in a sibling repo, UCBProject/ObjMesh.

Layout

training_fp/
  oakink/   100 files   (A* C* O* S* Y* — OakInk object IDs)
  dexycb/    20 files   (ycb_dex_01 ... ycb_dex_20 — DexYCB grasp objects)
human_prior_fp/
  A01001.hdf5 ... Y35037.hdf5   100 oakink files
  ycb_dex_01.hdf5 ... ycb_dex_20.hdf5   20 dexycb files

HDF5 schema

`training_fp/{ds}/{obj}.hdf5` (training-ready)

key	shape	dtype	meaning
`point_cloud`	(4096, 3)	float32	4096 surface samples of the SAM3D object mesh (metric m, mesh canonical frame)
`normals`	(4096, 3)	float32	per-point unit normals
`human_prior`	(4096,)	float32	per-point contact probability in [0, 1] (Gaussian-smoothed, per-object max-normalised)
`robot_gt`	(4096,)	float32	all-zero placeholder (no robot ground truth for this regime)
`force_center`	(3,)	float32	centroid of mesh verts with `contact_smooth >= 80th percentile`
attr `object`	str	—	object id (e.g. `ycb_dex_14` or `A01001`)

`human_prior_fp/{obj}.hdf5` (inference-ready, indexed by object only)

Same five fields as above. Same content as training_fp/{ds}/{obj}.hdf5, but flattened across datasets so that inference/predictor.py can read by object id alone.

Provenance

Generated by:

data/batch_depth_pro.py → data/batch_haptic.py → tools/batch_obj_pose.py → data/batch_align_mano_fp.py
Source raw data: DexYCB (NVIDIA, CC-BY-NC-4.0) + OakInk (CVPR 2022).
Phase 1A run on lab RTX 5090; OakInk completed 2026-05-10, DexYCB completed 2026-05-14.

HO3D-v3 and ARCTIC are not included here — those will be added when the partner runs their Phase 1A on those datasets.

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