Datasets:

SWIRL-Lab
/

bigym2-agent-rollouts

Error code:   StreamingRowsError
Exception:    TypeError
Message:      Couldn't cast array of type double to null
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 2315, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2071, in cast_array_to_feature
                  _c(children[name] if name in array_fields else null_array, subfeature)
                  ~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2155, in cast_array_to_feature
                  return array_cast(
                      array,
                  ...<2 lines>...
                      allow_decimal_to_str=allow_decimal_to_str,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2014, in array_cast
                  raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
              TypeError: Couldn't cast array of type double to null

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BiGym 2.0

BiGym 2.0 — Coding-Agent Rollouts

Every evaluation episode behind the coding-agent results of BiGym 2.0: the frozen program from each development session, replayed on all 100 hidden evaluation seeds. Two agents (Claude Opus 5.5 via Claude Code, GPT-6 Astra via Codex CLI), nine tasks, three sessions each: 54 programs, 5,400 episodes. Each replay reproduces the scored outcome of its seed.

Layout

manifest.csv                                    one row per episode
episodes/<task>/metadata.json                   the environment the task's episodes ran in
episodes/<task>/<session>_seed<seed>.npz        simulator state and actions
viser/<task>/<session>_seed<seed>.viser         3D replay for the viser player
viser/<task>/<session>_seed<seed>.viser.json    its frame count, length and camera
trace/<task>/<session>_seed<seed>.json          lines the program ran at each control step

Sessions are named opus-s1 … opus-s3 and astra-s1 … astra-s3; seeds run from 620000 to 620099.

Programs of all agents

policies/ holds the final scored program of every session for seven agents on all 20 tasks (3 sessions each, 420 sessions): Claude Opus 5.5, GPT-6 Astra, GPT-6 Sol, GPT-6.1 Sol, Kimi K3.1 Pro, DeepSeek V4.1 Flash (1017) and GPT-6.1 Terra.

policies.csv                                    one row per session: agent, harness, effort, success rate, cost, tokens
policies/<task>/<session>/policy.py             the scored program, with the helper modules it imports
policies/<task>/<session>/episodes.csv          its 100 hidden seeds: seed, success, length, termination, fell

Sessions are named <agent>-s1 … <agent>-s3. A session that ended without a scorable program has a row in policies.csv with success 0 and no folder. Running a program on the seeds 620000–620099 reproduces its episodes.

Watching them

Each episodes/<task>/ folder opens in the BiGym 2.0 viewer, with every episode's seed, outcome and length in its list:

hf download SWIRL-Lab/bigym2-agent-rollouts --repo-type dataset --include "episodes/move_plate/*" --local-dir bigym2-agent-rollouts
uv run bigym-view --demo-dir bigym2-agent-rollouts/episodes/move_plate

--include "episodes/move_plate/astra-s1_*" fetches one session only. The viewer is in the code repository.

Episodes

One .npz per episode, stepped at the 50 Hz control rate (step i is at i/50 s). No images are stored: the full simulator state is, so any camera at any resolution can be re-rendered.

Key	Shape
`full_qpos`	(T+1, nq)	positions of the robot and every object
`full_qvel`	(T+1, nv)	velocities
`action`	(T, 20 or 21)	joint targets the program sent
`reward`	(T, 1)	per-step reward
`seed`, `length`	scalar	evaluation seed, T
`success`, `fell`	scalar	1.0 if the goal state held for one second / if the robot fell
`termination`	scalar	`success`, `timeout`, `fell`, `physics_error` or `terminated`

nq and nv depend on the task's objects. reach_target_*, move_plate and drawer_top_* use a 20-dim action; the other tasks add a torso-pitch command. metadata.json holds the environment configuration (env_config), which EnvConfig.from_metadata reads back.

Code traces

Which lines of its program each episode ran, step by step: the episode was re-run with the frozen program under Python's sys.settrace, and a trace is kept only if that re-run's state trajectory is identical to the scored one, frame for frame. 5,347 of the 5,400 episodes have one. The other 53 (43 of them reach_target_multi_modal/opus-s1) re-run with float drift from about step 42: same length and outcome, up to a few millimetres apart.

Line numbers refer to the program as the project page lists it: policy.py, then each helper module in name order, each after three extra lines (a blank line, a # ──────── <file> ──────── divider, a blank line).

Key
`length`, `success`	episode length T, outcome
`sets`	the distinct sets of lines run in one step
`steps`	for each of the T steps, the index of its set in `sets`
`window`, `segments`	for display: phases of the episode, as `[from, to, line, [methods]]` over runs of the lines run in the last `window` steps

Manifest

manifest.csv has task, session, model, harness, program_version (the development iteration that was frozen and scored), seed, success, length, and the paths of the episode and its replay.

The human VR demonstrations of the same tasks are in SWIRL-Lab/bigym-g1-native60.

Citation

@article{zhang2026bigym2,
  title   = {BiGym 2.0: Benchmarking Learned and Agent-Developed Policies for Humanoid Household Manipulation},
  author  = {Zhang, Zexi and Zhu, Zecheng and Chen, Zidong and Tuya, Zulkhuu and James, Stephen},
  journal = {arXiv preprint arXiv:2610.07594},
  year    = {2026}
}

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Paper for SWIRL-Lab/bigym2-agent-rollouts

BiGym 2.0: Benchmarking Learned and Agent-Developed Policies for Humanoid Household Manipulation

Paper • 2610.07594 • Published 29 days ago