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The dataset generation failed
Error code: DatasetGenerationError
Exception: ValueError
Message: Expected object or value
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1835, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 621, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 281, in _generate_tables
examples = [ujson_loads(line) for line in batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
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 1379, 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 956, 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 1702, 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 1887, 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.
update int64 | transitions int64 | elapsed_s float64 | update_wall_s float64 | loss dict | rollout_wall_s float64 | optimizer_wall_s float64 | learning_rate float64 | kl dict | learning_diagnostics dict | rollout_diagnostics dict | exploration dict | reward_mean float64 | geometry_reward float64 | active_object_reward float64 | wrist_error_m float64 | active_object_error_m float64 | invalid_transition_fraction float64 | command_clipped_fraction float64 | contact_reward float64 | object_translation_reward float64 | object_rotation_reward float64 | object_translation_reward_weighted float64 | object_rotation_reward_weighted float64 | object_reward_weighted float64 | reward_before_invalid float64 | task_reward float64 | command_change_penalty float64 | geometry_reward_weighted float64 | diagnostic_valid_samples dict | diagnostic_nonfinite_samples dict | episodes int64 | invalid_reason_counts unknown | invalid_total int64 | episode_statistics dict |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 131,072 | 204.32738 | 204.290324 | {
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"entropy": -12.9239747524
} | 204.150845 | 0.139425 | 0.0003 | {
"minibatches": 8,
"mean": 0.0022014524,
"maximum": 0.0031280017,
"minimum": 0
} | {
"scope": "Passive pre-clipping gradients/ratios and analytic distribution KL; rollout pre-update explained variance",
"critic_explained_variance": 0,
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"maximum": 0.0146179199,
"minibatches": 8
},
"kl": {
"mean": 0.0022014524,
"maximum": 0.003128001... | {
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"maximum": 1,
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"nonfinite_samples": 0
},
"termination/task_failure_candidate": {
"mean": 0.0077896118,
"rms": 0.0882587776,
"maximum": 1,
"valid_samples": 131072,
"nonfinite_s... | {
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0.30026751... | 2.034757 | 0.940293 | 0.451422 | 0.004548 | 0.017172 | 0.000023 | 0.039632 | 0.281276 | 0.478765 | 0.369394 | 1.438046 | 0.369924 | 1.80797 | 2.086378 | 0.281344 | 0.002965 | 0 | {
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"object_translation_reward_weighte... | 1,024 | {
"backend_invalid": 3,
"contact_invalid": 3,
"nonfinite_state": 3,
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} | 3 | {
"return": 260.4169396386,
"length": 119.892578125,
"coverage": 0.2984610142,
"completed": 0,
"invalid": 0.0029296875,
"task_failed": 0.9970703125,
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} |
2 | 262,144 | 406.970521 | 202.574741 | {
"value": 1562.6530761719,
"surrogate": -0.0005507429,
"entropy": -12.9012266397
} | 202.495557 | 0.079142 | 0.0003 | {
"minibatches": 8,
"mean": 0.0021616649,
"maximum": 0.0040219836,
"minimum": 0.0000175686
} | {
"scope": "Passive pre-clipping gradients/ratios and analytic distribution KL; rollout pre-update explained variance",
"critic_explained_variance": 0.0011281967,
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"maximum": 0.0298461914,
"minibatches": 8
},
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"maximum": ... | {
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},
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"mean": 0.0078125,
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"maximum": 1,
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0.3004855514,... | 2.037395 | 0.940382 | 0.451906 | 0.004642 | 0.01716 | 0 | 0.037749 | 0.281982 | 0.479455 | 0.369255 | 1.440098 | 0.369782 | 1.809881 | 2.088965 | 0.282047 | 0.002963 | 0 | {
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"object_translation_reward_weighte... | 1,024 | {} | 3 | {
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"coverage": 0.2987162982,
"completed": 0,
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} |
3 | 393,216 | 613.035618 | 206.044584 | {
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"surrogate": -0.0004184651,
"entropy": -12.8798481226
} | 205.965518 | 0.079026 | 0.0003 | {
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} | {
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},
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0.30059850... | 2.035796 | 0.939059 | 0.451411 | 0.004708 | 0.017163 | 0.000015 | 0.03726 | 0.282324 | 0.479018 | 0.368591 | 1.438812 | 0.369123 | 1.807935 | 2.087434 | 0.282403 | 0.002908 | 0 | {
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} | 5 | {
"return": 260.2090485431,
"length": 119.8408203125,
"coverage": 0.2983358034,
"completed": 0,
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} |
4 | 524,288 | 818.038799 | 204.925998 | {
"value": 1528.9971618652,
"surrogate": -0.0003217035,
"entropy": -12.8587856293
} | 204.846894 | 0.079064 | 0.0003 | {
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"mean": 0.0019220494,
"maximum": 0.0037604102,
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} | {
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},
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0.300582289... | 2.040163 | 0.940413 | 0.451898 | 0.004686 | 0.017131 | 0.000023 | 0.037012 | 0.284754 | 0.479593 | 0.368816 | 1.440564 | 0.36936 | 1.809925 | 2.091876 | 0.284831 | 0.002906 | 0 | {
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} | 8 | {
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5 | 655,360 | 1,023.646297 | 205.585573 | {
"value": 1484.4681091309,
"surrogate": -0.0002647404,
"entropy": -12.8399251699
} | 205.50636 | 0.079176 | 0.0003 | {
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} | {
"scope": "Passive pre-clipping gradients/ratios and analytic distribution KL; rollout pre-update explained variance",
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... | 2.045766 | 0.944724 | 0.452181 | 0.004447 | 0.017158 | 0.000023 | 0.038063 | 0.289251 | 0.479757 | 0.369455 | 1.441055 | 0.369998 | 1.811054 | 2.097505 | 0.28933 | 0.002912 | 0 | {
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"object_translation_reward_weighte... | 1,024 | {
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} | 11 | {
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6 | 786,432 | 1,231.105381 | 207.432026 | {
"value": 1407.6136016846,
"surrogate": -0.0004100719,
"entropy": -12.8194031715
} | 207.352189 | 0.079789 | 0.0003 | {
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} | {
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0.3009600639... | 2.199153 | 0.946137 | 0.488838 | 0.004393 | 0.016738 | 0.000023 | 0.036584 | 0.296241 | 0.518964 | 0.398461 | 1.558534 | 0.399001 | 1.957535 | 2.250999 | 0.296325 | 0.002898 | 0 | {
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"object_translation_reward_weighte... | 1,026 | {
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} | 14 | {
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7 | 917,504 | 1,438.017576 | 206.890889 | {
"value": 1301.6952056885,
"surrogate": -0.0003825882,
"entropy": -12.7991422415
} | 206.811952 | 0.078919 | 0.0003 | {
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"mean": 0.0025548171,
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} | {
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0.3014594018,... | 2.261662 | 0.946816 | 0.502482 | 0.004318 | 0.016494 | 0.000038 | 0.036607 | 0.304239 | 0.532928 | 0.411147 | 1.600443 | 0.411638 | 2.012081 | 2.313563 | 0.304322 | 0.002893 | 0 | {
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} | 19 | {
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8 | 1,048,576 | 1,644.9814 | 206.941121 | {
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9 | 1,179,648 | 1,853.76489 | 208.757129 | {
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} | 208.677857 | 0.079237 | 0.0003 | {
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} | {"scope":"Passive pre-clipping gradients/ratios and analytic distribution KL; rollout pre-update exp(...TRUNCATED) | {"termination/task_failure":{"mean":0.0078201294,"rms":0.0884314955,"maximum":1.0,"valid_samples":13(...TRUNCATED) | {"mode":"revo3_iid","marginal_std":{"per_joint":[0.020167442,0.0201238282,0.020033706,0.0202428848,0(...TRUNCATED) | 2.273772 | 0.947157 | 0.500196 | 0.004279 | 0.016476 | 0.000015 | 0.039635 | 0.325498 | 0.532347 | 0.403743 | 1.598681 | 0.404211 | 2.002893 | 2.325637 | 0.325588 | 0.00288 | 0 | {"reward_mean":131072,"geometry_reward":131070,"active_object_reward":131072,"wrist_error_m":131070,(...TRUNCATED) | {"reward_mean":0,"geometry_reward":2,"active_object_reward":0,"wrist_error_m":2,"active_object_error(...TRUNCATED) | 1,027 | {
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10 | 1,310,720 | 2,062.646982 | 208.860196 | {
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} | {"scope":"Passive pre-clipping gradients/ratios and analytic distribution KL; rollout pre-update exp(...TRUNCATED) | {"termination/task_failure":{"mean":0.0078277588,"rms":0.0884746223,"maximum":1.0,"valid_samples":13(...TRUNCATED) | {"mode":"revo3_iid","marginal_std":{"per_joint":[0.0201946795,0.0201509073,0.0200496167,0.0202644505(...TRUNCATED) | 2.28618 | 0.946715 | 0.501154 | 0.004255 | 0.016333 | 0.000038 | 0.040285 | 0.334374 | 0.533131 | 0.405221 | 1.600992 | 0.405702 | 2.006693 | 2.338383 | 0.334488 | 0.002874 | 0 | {"reward_mean":131072,"geometry_reward":131067,"active_object_reward":131072,"wrist_error_m":131067,(...TRUNCATED) | {"reward_mean":0,"geometry_reward":5,"active_object_reward":0,"wrist_error_m":5,"active_object_error(...TRUNCATED) | 1,031 | {
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} | 28 | {"return":261.8223211925,"length":119.620756547,"coverage":0.2977820468,"completed":0.0,"invalid":0.(...TRUNCATED) |
End of preview.
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Check out the documentation for more information.
DexSys checkpoints
Checkpoint transfer for the private DexSys Co-tracking code repository.
Installation, model interfaces, case preparation and evaluation instructions are maintained in its README on main.
This repository is public: anyone can download the model files. Current uploads contain only trained checkpoints and their required SHA-256 sidecars. No tokens, new scene data, training logs, trajectories or replays are uploaded with these checkpoints.
| Group | Files | Scope |
|---|---|---|
| B | checkpoint, checksum | Uniform 50 Hz; joint residuals |
| C | checkpoint, checksum | Uniform 50 Hz; half-speed reference |
| E | checkpoint, checksum | Uniform 50 Hz; half-speed C2 reference and wrist-pose residuals |
All three are Revo3 co-tracking seed42, update100, for the prepared 125315 fourth window. Keep the .json beside the .pt. Use the matching co-tracking code and case; these checkpoints do not use the older inference export format.
Earlier bundles remain in their original directories for compatibility. The source case used to regenerate the current inputs is pinned at revision 7db38ad3b3239794c4b79c09422a4587ebca7bd4 under revo3/flexiv-right-revo3-125315-w4-nullspace-policy50-u100/case.
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