MacroSwan/cablepilot
Robotics • Updated
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
format: string
card: string
card_sha: string
lineage: struct<parent: string, by: string, why: string>
child 0, parent: string
child 1, by: string
child 2, why: string
tuning: string
tuning_sha: string
body: string
model: string
world: string
seed: int64
episodes: int64
score: struct<done: int64, of: int64, done_frac: double, s_per_item: double, furniture_contact_ticks: int64 (... 24 chars omitted)
child 0, done: int64
child 1, of: int64
child 2, done_frac: double
child 3, s_per_item: double
child 4, furniture_contact_ticks: int64
child 5, max_tension_N: double
compute: struct<plan_ms_mean: double, plan_ms_p95: double, perception_ms_mean: double>
child 0, plan_ms_mean: double
child 1, plan_ms_p95: double
child 2, perception_ms_mean: double
env: struct<python: string, machine: string, torch: string, numpy: string, mujoco: string, reference_sim: (... 6 chars omitted)
child 0, python: string
child 1, machine: string
child 2, torch: string
child 3, numpy: string
child 4, mujoco: string
child 5, reference_sim: bool
binding: struct<held: list<item: string>, sites: struct<toy_box: string>, notes: list<item: string>>
child 0, held: list<item: string>
child 0, item: string
child 1, sites: struct<toy_box: string>
child 0, toy_box: string
child 2, notes: list<item: string>
child 0, item: string
skills: list<item: list<item: string>>
child 0, item: list<item: string>
child 0, item: string
unwatched: list<item: null>
chi
...
g
child 11, date: timestamp[s]
child 12, sim: string
child 13, reference_sim: bool
child 14, result: string
child 15, media: string
child 2, fold_clothes: struct<needs: struct<predicates: list<item: string>, hands: int64>, lineage: struct<parent: string, (... 103 chars omitted)
child 0, needs: struct<predicates: list<item: string>, hands: int64>
child 0, predicates: list<item: string>
child 0, item: string
child 1, hands: int64
child 1, lineage: struct<parent: string, by: string, why: string>
child 0, parent: string
child 1, by: string
child 2, why: string
child 2, tunings: list<item: null>
child 0, item: null
child 3, results: list<item: null>
child 0, item: null
child 4, best: list<item: null>
child 0, item: null
child 3, pen_in_cup: struct<needs: struct<predicates: list<item: string>, hands: int64>, lineage: null, tunings: list<ite (... 60 chars omitted)
child 0, needs: struct<predicates: list<item: string>, hands: int64>
child 0, predicates: list<item: string>
child 0, item: string
child 1, hands: int64
child 1, lineage: null
child 2, tunings: list<item: null>
child 0, item: null
child 3, results: list<item: null>
child 0, item: null
child 4, best: list<item: null>
child 0, item: null
to
{'format': Value('string'), 'built': Value('timestamp[s]'), 'bodies': List({'body': Value('string'), 'pilot': Value('string'), 'status': Value('string'), 'carrier': Value('string'), 'machines': List(Value('string')), 'rigs': List({'rig': Value('string'), 'hands': Value('int64'), 'hand': Value('string'), 'status': Value('string')}), 'predicates': List(Value('string')), 'predicates_source': Value('string'), 'model': {'params': Value('int64'), 'train': Value('string')}, 'note': Value('string')}), 'tasks': {'avoid_furniture': {'needs': {'predicates': List(Value('string')), 'hands': Value('int64')}, 'lineage': {'parent': Value('null'), 'by': Value('string'), 'why': Value('string')}, 'tunings': List(Value('null')), 'results': List(Value('null')), 'best': List(Value('null'))}, 'clean_the_room': {'needs': {'predicates': List(Value('string')), 'hands': Value('int64')}, 'lineage': {'parent': Value('string'), 'by': Value('string'), 'why': Value('string')}, 'tunings': List(Value('string')), 'results': List({'id': Value('string'), 'body': Value('string'), 'world': Value('string'), 'seed': Value('int64'), 'episodes': Value('int64'), 'score': {'done': Value('int64'), 'of': Value('int64'), 'done_frac': Value('float64'), 's_per_item': Value('float64'), 'furniture_contact_ticks': Value('int64'), 'max_tension_N': Value('float64')}, 'compute': {'plan_ms_mean': Value('float64'), 'plan_ms_p95': Value('float64'), 'perception_ms_mean': Value('float64')}, 'card_sha': Value('string'), 'tuning_sha': Va
...
('int64'), 'world': Value('string'), 'result': Value('string')}, 'arm': {'state': Value('string')}, 'dog': {'state': Value('string')}, 'drone': {'state': Value('string')}, 'human': {'state': Value('string')}}, 'fold_clothes': {'cable': {'state': Value('string'), 'missing': List(Value('string'))}, 'arm': {'state': Value('string')}, 'dog': {'state': Value('string')}, 'drone': {'state': Value('string')}, 'human': {'state': Value('string')}}, 'pen_in_cup': {'cable': {'state': Value('string')}, 'arm': {'state': Value('string')}, 'dog': {'state': Value('string')}, 'drone': {'state': Value('string')}, 'human': {'state': Value('string')}}}, 'skills': {'folded': {'cards': List(Value('string')), 'bodies': List(Value('null'))}, 'grasp': {'cards': List(Value('string')), 'bodies': List(Value('string'))}, 'inside': {'cards': List(Value('string')), 'bodies': List(Value('string'))}, 'laid_flat': {'cards': List(Value('string')), 'bodies': List(Value('null'))}, 'near': {'cards': List(Value('string')), 'bodies': List(Value('null'))}, 'on_top': {'cards': List(Value('string')), 'bodies': List(Value('null'))}, 'pre_grasp': {'cards': List(Value('string')), 'bodies': List(Value('string'))}, 'pre_inside': {'cards': List(Value('string')), 'bodies': List(Value('string'))}, 'release': {'cards': List(Value('string')), 'bodies': List(Value('string'))}, 'see': {'cards': List(Value('string')), 'bodies': List(Value('string'))}, 'support_both': {'cards': List(Value('string')), 'bodies': List(Value('null'))}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
format: string
card: string
card_sha: string
lineage: struct<parent: string, by: string, why: string>
child 0, parent: string
child 1, by: string
child 2, why: string
tuning: string
tuning_sha: string
body: string
model: string
world: string
seed: int64
episodes: int64
score: struct<done: int64, of: int64, done_frac: double, s_per_item: double, furniture_contact_ticks: int64 (... 24 chars omitted)
child 0, done: int64
child 1, of: int64
child 2, done_frac: double
child 3, s_per_item: double
child 4, furniture_contact_ticks: int64
child 5, max_tension_N: double
compute: struct<plan_ms_mean: double, plan_ms_p95: double, perception_ms_mean: double>
child 0, plan_ms_mean: double
child 1, plan_ms_p95: double
child 2, perception_ms_mean: double
env: struct<python: string, machine: string, torch: string, numpy: string, mujoco: string, reference_sim: (... 6 chars omitted)
child 0, python: string
child 1, machine: string
child 2, torch: string
child 3, numpy: string
child 4, mujoco: string
child 5, reference_sim: bool
binding: struct<held: list<item: string>, sites: struct<toy_box: string>, notes: list<item: string>>
child 0, held: list<item: string>
child 0, item: string
child 1, sites: struct<toy_box: string>
child 0, toy_box: string
child 2, notes: list<item: string>
child 0, item: string
skills: list<item: list<item: string>>
child 0, item: list<item: string>
child 0, item: string
unwatched: list<item: null>
chi
...
g
child 11, date: timestamp[s]
child 12, sim: string
child 13, reference_sim: bool
child 14, result: string
child 15, media: string
child 2, fold_clothes: struct<needs: struct<predicates: list<item: string>, hands: int64>, lineage: struct<parent: string, (... 103 chars omitted)
child 0, needs: struct<predicates: list<item: string>, hands: int64>
child 0, predicates: list<item: string>
child 0, item: string
child 1, hands: int64
child 1, lineage: struct<parent: string, by: string, why: string>
child 0, parent: string
child 1, by: string
child 2, why: string
child 2, tunings: list<item: null>
child 0, item: null
child 3, results: list<item: null>
child 0, item: null
child 4, best: list<item: null>
child 0, item: null
child 3, pen_in_cup: struct<needs: struct<predicates: list<item: string>, hands: int64>, lineage: null, tunings: list<ite (... 60 chars omitted)
child 0, needs: struct<predicates: list<item: string>, hands: int64>
child 0, predicates: list<item: string>
child 0, item: string
child 1, hands: int64
child 1, lineage: null
child 2, tunings: list<item: null>
child 0, item: null
child 3, results: list<item: null>
child 0, item: null
child 4, best: list<item: null>
child 0, item: null
to
{'format': Value('string'), 'built': Value('timestamp[s]'), 'bodies': List({'body': Value('string'), 'pilot': Value('string'), 'status': Value('string'), 'carrier': Value('string'), 'machines': List(Value('string')), 'rigs': List({'rig': Value('string'), 'hands': Value('int64'), 'hand': Value('string'), 'status': Value('string')}), 'predicates': List(Value('string')), 'predicates_source': Value('string'), 'model': {'params': Value('int64'), 'train': Value('string')}, 'note': Value('string')}), 'tasks': {'avoid_furniture': {'needs': {'predicates': List(Value('string')), 'hands': Value('int64')}, 'lineage': {'parent': Value('null'), 'by': Value('string'), 'why': Value('string')}, 'tunings': List(Value('null')), 'results': List(Value('null')), 'best': List(Value('null'))}, 'clean_the_room': {'needs': {'predicates': List(Value('string')), 'hands': Value('int64')}, 'lineage': {'parent': Value('string'), 'by': Value('string'), 'why': Value('string')}, 'tunings': List(Value('string')), 'results': List({'id': Value('string'), 'body': Value('string'), 'world': Value('string'), 'seed': Value('int64'), 'episodes': Value('int64'), 'score': {'done': Value('int64'), 'of': Value('int64'), 'done_frac': Value('float64'), 's_per_item': Value('float64'), 'furniture_contact_ticks': Value('int64'), 'max_tension_N': Value('float64')}, 'compute': {'plan_ms_mean': Value('float64'), 'plan_ms_p95': Value('float64'), 'perception_ms_mean': Value('float64')}, 'card_sha': Value('string'), 'tuning_sha': Va
...
('int64'), 'world': Value('string'), 'result': Value('string')}, 'arm': {'state': Value('string')}, 'dog': {'state': Value('string')}, 'drone': {'state': Value('string')}, 'human': {'state': Value('string')}}, 'fold_clothes': {'cable': {'state': Value('string'), 'missing': List(Value('string'))}, 'arm': {'state': Value('string')}, 'dog': {'state': Value('string')}, 'drone': {'state': Value('string')}, 'human': {'state': Value('string')}}, 'pen_in_cup': {'cable': {'state': Value('string')}, 'arm': {'state': Value('string')}, 'dog': {'state': Value('string')}, 'drone': {'state': Value('string')}, 'human': {'state': Value('string')}}}, 'skills': {'folded': {'cards': List(Value('string')), 'bodies': List(Value('null'))}, 'grasp': {'cards': List(Value('string')), 'bodies': List(Value('string'))}, 'inside': {'cards': List(Value('string')), 'bodies': List(Value('string'))}, 'laid_flat': {'cards': List(Value('string')), 'bodies': List(Value('null'))}, 'near': {'cards': List(Value('string')), 'bodies': List(Value('null'))}, 'on_top': {'cards': List(Value('string')), 'bodies': List(Value('null'))}, 'pre_grasp': {'cards': List(Value('string')), 'bodies': List(Value('string'))}, 'pre_inside': {'cards': List(Value('string')), 'bodies': List(Value('string'))}, 'release': {'cards': List(Value('string')), 'bodies': List(Value('string'))}, 'see': {'cards': List(Value('string')), 'bodies': List(Value('string'))}, 'support_both': {'cards': List(Value('string')), 'bodies': List(Value('null'))}}}
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.
pilot tasks
Robot tasks written as cards, improved in the open by people and their agents. Each improvement is scored and traceable, and every pilot reads the same cards: cablepilot (cable robots) today, armpilot and dronepilot next.
A card says what a job is, in a vocabulary every body shares. Each body says how, with its own world model and planner. A tuning file says how much for one body. A result says how it went. So a new task starts from the best card so far, not from scratch, and a subtask written for one robot is there for the next.
tasks/<task>/
card.yaml body-free: objects, stages (each a skill), fallbacks, done test, lineage
tuning/<body>.yaml that body's numbers (cost weights, thresholds), with lineage
results/<id>.json one scored run (format pilot-result/1): score, compute, world, seed, card + tuning hashes
media/<id>.mp4 its film (the plan in purple)
board.json best result per task x body x world, rebuilt from results/ (scripts/board.py)
Tasks
| task | bodies | best so far (sim) | note |
|---|---|---|---|
| clean_the_room | cable | 29/36 toys boxed, bedroom, 3 episodes | requested by the Stringman community; sorting is the next card |
| avoid_furniture | (card only) | open | requested by the Stringman community: place to place, touching nothing. cablepilot check lists what the cable body is missing (a near skill, a furnished room): the first open job |
| pen_in_cup | (card only) | no hub result yet | armpilot's card, unchanged; the cable robot runs it but cannot watch aligned(pen, up) yet. Its arm tuning comes with armpilot's release |
A card
task: clean_the_room
lineage: {parent: "worldpilot:cards/home/clean_the_room.yaml", by: Ethgar, why: "runnable on today's skills"}
objects:
stray: {kind: any, role: held, shape: any, movable: true, each: out_of_place}
toy_box: {kind: box, role: site, shape: open_container, movable: false}
stages:
- {name: observe, goals: [{see: [head_camera, stray]}]}
- {name: pre_grasp, goals: [{pre_grasp: [hand, stray]}], avoid: [{avoid: [hand, toy_box]}]}
- {name: grasp, goals: [{grasp: [hand, stray]}]}
- {name: over_box, goals: [{pre_inside: [stray, toy_box.interior]}], invariants: [{grasp: [hand, stray]}]}
- {name: release, goals: [{release: [hand, stray]}]}
fallbacks:
- {when: {broken: {grasp: [hand, stray]}}, go: pre_grasp}
done: {inside: [stray, toy_box.interior]}
No metres, colours or positions: the body's eyes bind stray and toy_box when it runs, and the body's tuning holds
the numbers. Predicates and skills: cablepilot/core/cards.py (PREDICATES, SKILLS).
Contributing (people and agents)
cablepilot fetch <task>, thencablepilot run tasks/<task>/card.yaml --world bedroomto reproduce the best result.- Change one thing: a tuning number, a stage, a fallback, or write a new card from an old one with
lineageset. - Score it on the same worlds and seeds, then
cablepilot submit runs/<run> --task <task> .... That opens a pull request here, and its page is the discussion thread. - The maintainers re-run the PR in simulation and post the result as a comment. Merged results update
board.json.
Good first contributions: a missing skill that cablepilot check reports on an existing card (window washing needs
wipe; folding needs support_both), a second seed or room for an existing result, or the first real-robot log.
Every card must stay body-free, every result must come with its result.json, and a measured regression is welcome
too.
- Downloads last month
- 57
Models trained or fine-tuned on MacroSwan/pilot-tasks