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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
run: string
command_vx: double
episodes: int64
seconds: double
randomized: bool
pushes: bool
forward_speed_error: double
mean_forward_speed: double
fall_rate: double
mean_time_up_fraction: double
mean_spin_rad_s: double
mean_turns: double
drop_rate: double
mean_time_held_fraction: double
mean_rotation_rad: double
to
{'run': Value('string'), 'episodes': Value('int64'), 'seconds': Value('float64'), 'randomized': Value('bool'), 'mean_spin_rad_s': Value('float64'), 'mean_rotation_rad': Value('float64'), 'mean_turns': Value('float64'), 'drop_rate': Value('float64'), 'mean_time_held_fraction': Value('float64')}
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
              run: string
              command_vx: double
              episodes: int64
              seconds: double
              randomized: bool
              pushes: bool
              forward_speed_error: double
              mean_forward_speed: double
              fall_rate: double
              mean_time_up_fraction: double
              mean_spin_rad_s: double
              mean_turns: double
              drop_rate: double
              mean_time_held_fraction: double
              mean_rotation_rad: double
              to
              {'run': Value('string'), 'episodes': Value('int64'), 'seconds': Value('float64'), 'randomized': Value('bool'), 'mean_spin_rad_s': Value('float64'), 'mean_rotation_rad': Value('float64'), 'mean_turns': Value('float64'), 'drop_rate': Value('float64'), 'mean_time_held_fraction': Value('float64')}
              because column names don't match

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Humanoid lab experiments

Every experiment from the first campaign of a home humanoid-robot project (September 2026): a simulated ROBOTIS OP3 humanoid that walks and a LEAP hand that spins a cube, trained with reinforcement learning on a home server's CPU and tested the way a real robot would be. Each experiment's pass bar was written before it ran, and failures are published as they came out. Simulation only.

The campaign's progress in 53 seconds.

Experiments

experiment what verdict
01-hand-spin 01 · Robot hand spins a cube fail
02-hardened-walking 02 · Hardened walking fail
03-robustness-matrix 03 · Robustness matrix fail
04-walking-mild-pushes 04-walking-mild-pushes crashed
05-walking-stronger-pushes 05 · Walking with stronger shoves fail
07-hand-brain-demo 07 · The hand obeys plain language pass

Each folder has README.md (the result against the bar and how it ran) and result.json; depending on the experiment also videos, contact sheets, evaluations (eval-*.json), speed sweeps, gesture checks and robustness matrices (robustness.json, robustness.md).

What was learned

  • The baseline walking policy survives servo delay, stiffness errors, extra mass, a slippery floor and sensor errors with 0 % falls, but any shove from 30 N knocks it over.
  • Training with strong kicks (1–5 m/s every 1–3 s) did not learn: 80.5 % falls, flat reward.
  • Gentler, staged shoves cut falls in the training simulator to 20.3 %, but on the robot's control loop the policy still falls at 30 N, and it walks in a narrower speed range.
  • The hand drops the cube in 2.3 % of randomized tests and spins at 0.94 rad/s (goal 1.0).
  • The hand follows a plain-language instruction through a locally running language model (pass).
  • Experiment 04 crashed: its training ran out of memory on a shared server.

Models

Paths inside the reports (runs/…, exports/…, reports/…) refer to the project's working tree; the videos and numbers are all here. Not a medical or care device; not a safety-rated controller.

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