BiGym 2.0: Benchmarking Learned and Agent-Developed Policies for Humanoid Household Manipulation
Paper • 2610.07594 • Published
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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
frames: int64
seconds: double
bytes: int64
camera: struct<position: list<item: double>, look_at: list<item: double>>
child 0, position: list<item: double>
child 0, item: double
child 1, look_at: list<item: double>
child 0, item: double
segments: null
v: int64
success: bool
steps: list<item: int64>
child 0, item: int64
sets: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
length: int64
window: int64
to
{'v': Value('int64'), 'window': Value('int64'), 'length': Value('int64'), 'success': Value('bool'), 'sets': List(List(Value('int64'))), 'steps': List(Value('int64')), 'segments': List(List(Json(decode=True)))}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
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
frames: int64
seconds: double
bytes: int64
camera: struct<position: list<item: double>, look_at: list<item: double>>
child 0, position: list<item: double>
child 0, item: double
child 1, look_at: list<item: double>
child 0, item: double
segments: null
v: int64
success: bool
steps: list<item: int64>
child 0, item: int64
sets: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
length: int64
window: int64
to
{'v': Value('int64'), 'window': Value('int64'), 'length': Value('int64'), 'success': Value('bool'), 'sets': List(List(Value('int64'))), 'steps': List(Value('int64')), 'segments': List(List(Json(decode=True)))}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1879, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
v int64 | window int64 | length int64 | success bool | sets list | steps list | segments list |
|---|---|---|---|---|---|---|
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2,
2,
2,
2,
2,
2,
2,
2,
2,
2... | [
[
0,
7,
38,
[]
],
[
8,
307,
47,
[]
],
[
308,
427,
38,
[]
],
[
428,
527,
38,
[]
],
[
528,
647,
38,
[]
],
[
648,
767,
38,
[]
],
[
768,
917,
38,
[]
],
[
918,
1457,
47,... |
End of preview.
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>/<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.
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, 47) | positions of the robot and every object |
full_qvel |
(T+1, 46) | velocities |
action |
(T, 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 |
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.
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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