Dataset Viewer
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: CastError
Message: Couldn't cast
schema_version: int64
source_run: string
source_episode: string
mode: string
source_sha256: struct<effective_config.json: string, sim_trace.npz: string, robot_environment_contact_trace.json: s (... 77 chars omitted)
child 0, effective_config.json: string
child 1, sim_trace.npz: string
child 2, robot_environment_contact_trace.json: string
child 3, gel_contact_trace.json: string
child 4, native_mechanics_monitor.json: string
trace_sampling: struct<frames: int64, rate_hz: int64, physics_dt_s: double, warning: string>
child 0, frames: int64
child 1, rate_hz: int64
child 2, physics_dt_s: double
child 3, warning: string
finding: string
object_outcome: struct<initial_xyz_m: list<item: double>, final_xyz_m: list<item: double>, final_displacement_m: dou (... 30 chars omitted)
child 0, initial_xyz_m: list<item: double>
child 0, item: double
child 1, final_xyz_m: list<item: double>
child 0, item: double
child 2, final_displacement_m: double
child 3, maximum_z_rise_m: double
loaded_robot_actor: struct<suffix: string, positive_force_samples: int64, first_t_s: double, last_t_s: double, peak_norm (... 229 chars omitted)
child 0, suffix: string
child 1, positive_force_samples: int64
child 2, first_t_s: double
child 3, last_t_s: double
child 4, peak_normal_force_n: double
child 5, peak_t_s: double
child 6, other_robot_actors_with_positive_egg_force_samples: list<item: null>
child 0, item: null
child 7, independent_packet_robot_force_
...
list<item: double>, point_left_pad_local_mm: lis (... 217 chars omitted)
child 0, t_s: double
child 1, force_n: double
child 2, point_world_m: list<item: double>
child 0, item: double
child 3, point_left_pad_local_mm: list<item: double>
child 0, item: double
child 4, nominal_full_gel_y_half_extent_mm: double
child 5, active_gel_y_half_extent_mm: double
child 6, face_midpoint_minus_carton_center_world_mm: list<item: double>
child 0, item: double
child 7, face_center_distance_mm: double
child 8, interpretation: string
tracking: struct<before_contact_0_to_5p8_s: struct<tcp_xyz_rmse_mm: double, tcp_xyz_max_mm: double, joint_targ (... 280 chars omitted)
child 0, before_contact_0_to_5p8_s: struct<tcp_xyz_rmse_mm: double, tcp_xyz_max_mm: double, joint_target_max_abs_error_rad: double>
child 0, tcp_xyz_rmse_mm: double
child 1, tcp_xyz_max_mm: double
child 2, joint_target_max_abs_error_rad: double
child 1, loaded_5p8_to_11p5_s: struct<tcp_xyz_rmse_mm: double, tcp_xyz_max_mm: double, joint_target_max_abs_error_rad: double>
child 0, tcp_xyz_rmse_mm: double
child 1, tcp_xyz_max_mm: double
child 2, joint_target_max_abs_error_rad: double
child 2, after_11p5_s: struct<tcp_xyz_rmse_mm: double, tcp_xyz_max_mm: double, joint_target_max_abs_error_rad: double>
child 0, tcp_xyz_rmse_mm: double
child 1, tcp_xyz_max_mm: double
child 2, joint_target_max_abs_error_rad: double
child 3, comparison: string
to
{'schema_version': Value('int64'), 'source_run': Value('string'), 'source_episode': Value('string'), 'mode': Value('string'), 'source_sha256': {'effective_config.json': Value('string'), 'sim_trace.npz': Value('string'), 'robot_environment_contact_trace.json': Value('string'), 'gel_contact_trace.json': Value('string')}, 'finding': Value('string'), 'trace_sampling': {'frames': Value('int64'), 'rate_hz': Value('int64'), 'physics_dt_s': Value('float64'), 'warning': Value('string')}, 'loaded_robot_actors': List({'exact_path': Value('string'), 'positive_contact_samples': Value('int64'), 'first_t_s': Value('float64'), 'last_t_s': Value('float64'), 'peak_normal_force_n': Value('float64'), 'peak_t_s': Value('float64')}), 'first_contact': {'t_s': Value('float64'), 'force_n': Value('float64'), 'point_world_m': List(Value('float64')), 'point_left_pad_local_mm': List(Value('float64')), 'nominal_full_gel_y_half_extent_mm': Value('float64'), 'active_gel_y_half_extent_mm': Value('float64'), 'face_midpoint_minus_carton_center_world_mm': List(Value('float64')), 'face_center_distance_mm': Value('float64'), 'interpretation': Value('string')}, 'object_outcome': {'initial_xyz_m': List(Value('float64')), 'final_xyz_m': List(Value('float64')), 'maximum_z_rise_m': Value('float64'), 'bin_support_peak_n': Value('float64'), 'pad_carton_force_peak_n': List(Value('float64'))}, 'tracking': {'before_contact_0_to_5p8_s': {'tcp_xyz_rmse_mm': Value('float64'), 'tcp_xyz_max_mm': Value('float64'), 'joint_target_max_abs_error_rad': Value('float64')}, 'loaded_5p8_to_11p5_s': {'tcp_xyz_rmse_mm': Value('float64'), 'tcp_xyz_max_mm': Value('float64'), 'joint_target_max_abs_error_rad': Value('float64')}, 'after_11p5_s': {'tcp_xyz_rmse_mm': Value('float64'), 'tcp_xyz_max_mm': Value('float64'), 'joint_target_max_abs_error_rad': Value('float64')}, 'comparison': Value('string')}, 'tactile_audit': {'pad_carton_force_peak_n': List(Value('float64')), 'gel_active_force_peak_n': List(Value('float64')), 'right_gel_total_observed_force_peak_n': Value('float64'), 'right_gel_observed_contact_scope': Value('string'), 'object_filter': Value('string')}, 'attribution': {'supported': Value('string'), 'not_established': Value('string'), 'next_gate': Value('string')}, 'source_and_runtime_unchanged': Value('bool')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
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 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
schema_version: int64
source_run: string
source_episode: string
mode: string
source_sha256: struct<effective_config.json: string, sim_trace.npz: string, robot_environment_contact_trace.json: s (... 77 chars omitted)
child 0, effective_config.json: string
child 1, sim_trace.npz: string
child 2, robot_environment_contact_trace.json: string
child 3, gel_contact_trace.json: string
child 4, native_mechanics_monitor.json: string
trace_sampling: struct<frames: int64, rate_hz: int64, physics_dt_s: double, warning: string>
child 0, frames: int64
child 1, rate_hz: int64
child 2, physics_dt_s: double
child 3, warning: string
finding: string
object_outcome: struct<initial_xyz_m: list<item: double>, final_xyz_m: list<item: double>, final_displacement_m: dou (... 30 chars omitted)
child 0, initial_xyz_m: list<item: double>
child 0, item: double
child 1, final_xyz_m: list<item: double>
child 0, item: double
child 2, final_displacement_m: double
child 3, maximum_z_rise_m: double
loaded_robot_actor: struct<suffix: string, positive_force_samples: int64, first_t_s: double, last_t_s: double, peak_norm (... 229 chars omitted)
child 0, suffix: string
child 1, positive_force_samples: int64
child 2, first_t_s: double
child 3, last_t_s: double
child 4, peak_normal_force_n: double
child 5, peak_t_s: double
child 6, other_robot_actors_with_positive_egg_force_samples: list<item: null>
child 0, item: null
child 7, independent_packet_robot_force_
...
list<item: double>, point_left_pad_local_mm: lis (... 217 chars omitted)
child 0, t_s: double
child 1, force_n: double
child 2, point_world_m: list<item: double>
child 0, item: double
child 3, point_left_pad_local_mm: list<item: double>
child 0, item: double
child 4, nominal_full_gel_y_half_extent_mm: double
child 5, active_gel_y_half_extent_mm: double
child 6, face_midpoint_minus_carton_center_world_mm: list<item: double>
child 0, item: double
child 7, face_center_distance_mm: double
child 8, interpretation: string
tracking: struct<before_contact_0_to_5p8_s: struct<tcp_xyz_rmse_mm: double, tcp_xyz_max_mm: double, joint_targ (... 280 chars omitted)
child 0, before_contact_0_to_5p8_s: struct<tcp_xyz_rmse_mm: double, tcp_xyz_max_mm: double, joint_target_max_abs_error_rad: double>
child 0, tcp_xyz_rmse_mm: double
child 1, tcp_xyz_max_mm: double
child 2, joint_target_max_abs_error_rad: double
child 1, loaded_5p8_to_11p5_s: struct<tcp_xyz_rmse_mm: double, tcp_xyz_max_mm: double, joint_target_max_abs_error_rad: double>
child 0, tcp_xyz_rmse_mm: double
child 1, tcp_xyz_max_mm: double
child 2, joint_target_max_abs_error_rad: double
child 2, after_11p5_s: struct<tcp_xyz_rmse_mm: double, tcp_xyz_max_mm: double, joint_target_max_abs_error_rad: double>
child 0, tcp_xyz_rmse_mm: double
child 1, tcp_xyz_max_mm: double
child 2, joint_target_max_abs_error_rad: double
child 3, comparison: string
to
{'schema_version': Value('int64'), 'source_run': Value('string'), 'source_episode': Value('string'), 'mode': Value('string'), 'source_sha256': {'effective_config.json': Value('string'), 'sim_trace.npz': Value('string'), 'robot_environment_contact_trace.json': Value('string'), 'gel_contact_trace.json': Value('string')}, 'finding': Value('string'), 'trace_sampling': {'frames': Value('int64'), 'rate_hz': Value('int64'), 'physics_dt_s': Value('float64'), 'warning': Value('string')}, 'loaded_robot_actors': List({'exact_path': Value('string'), 'positive_contact_samples': Value('int64'), 'first_t_s': Value('float64'), 'last_t_s': Value('float64'), 'peak_normal_force_n': Value('float64'), 'peak_t_s': Value('float64')}), 'first_contact': {'t_s': Value('float64'), 'force_n': Value('float64'), 'point_world_m': List(Value('float64')), 'point_left_pad_local_mm': List(Value('float64')), 'nominal_full_gel_y_half_extent_mm': Value('float64'), 'active_gel_y_half_extent_mm': Value('float64'), 'face_midpoint_minus_carton_center_world_mm': List(Value('float64')), 'face_center_distance_mm': Value('float64'), 'interpretation': Value('string')}, 'object_outcome': {'initial_xyz_m': List(Value('float64')), 'final_xyz_m': List(Value('float64')), 'maximum_z_rise_m': Value('float64'), 'bin_support_peak_n': Value('float64'), 'pad_carton_force_peak_n': List(Value('float64'))}, 'tracking': {'before_contact_0_to_5p8_s': {'tcp_xyz_rmse_mm': Value('float64'), 'tcp_xyz_max_mm': Value('float64'), 'joint_target_max_abs_error_rad': Value('float64')}, 'loaded_5p8_to_11p5_s': {'tcp_xyz_rmse_mm': Value('float64'), 'tcp_xyz_max_mm': Value('float64'), 'joint_target_max_abs_error_rad': Value('float64')}, 'after_11p5_s': {'tcp_xyz_rmse_mm': Value('float64'), 'tcp_xyz_max_mm': Value('float64'), 'joint_target_max_abs_error_rad': Value('float64')}, 'comparison': Value('string')}, 'tactile_audit': {'pad_carton_force_peak_n': List(Value('float64')), 'gel_active_force_peak_n': List(Value('float64')), 'right_gel_total_observed_force_peak_n': Value('float64'), 'right_gel_observed_contact_scope': Value('string'), 'object_filter': Value('string')}, 'attribution': {'supported': Value('string'), 'not_established': Value('string'), 'next_gate': Value('string')}, 'source_and_runtime_unchanged': Value('bool')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
HapticWAM — archived result media
Renamed from
armteam/phantom-resultson 2026-09-19, when the project's working name PHANTOM became HapticWAM (Haptic World-Action Model). The old id still redirects. The Python package and CLI keep the namephantom, so paths, checkpoint names and git blob ids below are unchanged.
This dataset holds the result media and source snapshots that were archived out of the HapticWAM code repository on 2026-09-18, so that the code repository stays a code repository: source, configs, tests and Markdown reports.
Nothing here is new data. Every file is a byte-for-byte copy of a file that used to be
tracked under docs/results/ in the HapticWAM git repository. The Markdown reports that
describe these results stayed in the code repository; only the artefacts they point at
(videos, plots, camera frames, arrays, evidence JSON, PDF renders and point-in-time
snapshots of source trees) moved here.
8 766 files, 1 049.3 MB.
Path mapping
The layout is 1:1 with the original repository:
repo_docs_results/<relative path> <-> docs/results/<relative path>
For example docs/results/rig_0916/wm_sensor_pred/panel.png is
repo_docs_results/rig_0916/wm_sensor_pred/panel.png here.
Fetching one file back
huggingface-cli download armteam/hapticwam-results \
repo_docs_results/<path> --repo-type dataset --local-dir .
(<path> is the path relative to docs/results/ in the code repository.) A direct URL
also works:
https://huggingface.co/datasets/armteam/hapticwam-results/resolve/main/repo_docs_results/<path>
To restore a whole result folder back into a checkout:
huggingface-cli download armteam/hapticwam-results --repo-type dataset \
--include 'repo_docs_results/rig_0916/**' --local-dir /tmp/restore
cp -a /tmp/restore/repo_docs_results/rig_0916 docs/results/rig_0916
MANIFEST.tsv
MANIFEST.tsv lists every archived file with four columns:
| column | meaning |
|---|---|
path |
path inside this dataset repository |
bytes |
file size |
sha256 |
SHA-256 of the file contents |
git_blob_sha1 |
the git blob id the file had in the code repository |
git_blob_sha1 lets you confirm that an archived file is exactly the object the code
repository used to track (git hash-object <file> reproduces it).
Contents
Per top-level folder, the archive holds rollout and rig video (.mp4), plots, overlays and
contact sheets (.png), camera frames (.jpg), numeric arrays (.npz), per-trial evidence
and trace files (.json, .jsonl, .log), PDF renders, and .py snapshots of the source
tree taken at the time of a run (e.g. */runtime_transfer/source_after/).
A small allowlist of files under docs/results/ was not archived and stays in the code
repository, because code or tests read it at runtime: frozen protocol/calibration JSON, the
few .py modules that tests import by path, registration_cad_faces.npz, and the frozen
input directories wrist_baseline_20260907/, sim_zoo_20260912/inputs/,
d435_factory_calibration_20260908/, d435_camera_audit_20260908/ and two frozen
campaign directories. All 444 Markdown reports also stayed.
Licence
.pysource snapshots: Apache-2.0, the licence of the HapticWAM code repository.- Everything else (video, images, arrays, evidence and trace files): CC-BY-4.0.
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