Physical Action Primitives (PAP)
Collection
Models and datasets for Physical Action Primitives: physical response operators for reusable robot action knowledge. • 5 items • Updated
Dataset Preview
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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowNotImplementedError
Message: Unsupported cast from float to list using function cast_list
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/hdf5/hdf5.py", line 87, in _generate_tables
yield Key(file_idx, batch_idx), cast_table_to_features(pa_table, self.info.features)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2285, in cast_table_to_features
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
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 2068, in cast_array_to_feature
_c(array.field(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 2152, 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 1944, in array_cast
return pa_type.wrap_array(_c(array, pa_type.storage_type))
~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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 1979, in array_cast
return pa.ListArray.from_arrays(array_offsets, _c(array.values, pa_type.value_type))
~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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 2015, in array_cast
return array.cast(pa_type)
~~~~~~~~~~^^^^^^^^^
File "pyarrow/array.pxi", line 1186, in pyarrow.lib.Array.cast
File "/usr/local/lib/python3.14/site-packages/pyarrow/compute.py", line 414, in cast
return call_function("cast", [arr], options, memory_pool)
File "pyarrow/_compute.pyx", line 604, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 399, in pyarrow._compute.Function.call
result = GetResultValue(
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowNotImplementedError: Unsupported cast from float to list using function cast_list
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.
action dict | controller dict | metadata dict | physical dict | replay dict | reporting_only dict | temporal dict | validity dict |
|---|---|---|---|---|---|---|---|
{"perturbation_from_source_controller_space":[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.05130302906036377(...TRUNCATED) | {"arm_target_qpos":[[-0.12662464380264282,0.8069928884506226,0.17910408973693848,-1.8018099069595337(...TRUNCATED) | {"branch_id":"ZXhwNl9mb3JtYWxfY29ycHVzX3YxOlBva2VDdWJlLXYxOnNlZWQtMTAwMDAwMDAwOnNhbWUtcGFyZW50OjA=",(...TRUNCATED) | {"contact_channel_mask":[[true,true,true,true,true,false,true],[true,true,true,true,true,false,true](...TRUNCATED) | {"sim_state":{"actors":{"cube":[[0.2420978546142578,0.058322906494140625,0.01999995857477188,0.99347(...TRUNCATED) | {"outcome":{"reward":[4.808187961578369,4.804165840148926,4.8039960861206055,4.808070659637451,4.818(...TRUNCATED) | {
"action_index": 0,
"frame_index": 0
} | {"action_clipped":[false,true,true,true,true,true,true,true,false,true,true,true,true,true,true,true(...TRUNCATED) |
{"perturbation_from_source_controller_space":[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.09641814231872559(...TRUNCATED) | {"arm_target_qpos":[[-0.1523386538028717,0.8422878980636597,0.2119300812482834,-1.7630207538604736,-(...TRUNCATED) | {"branch_id":"ZXhwNl9mb3JtYWxfY29ycHVzX3YxOlBva2VDdWJlLXYxOnNlZWQtMTAwMDAwMDAwOnNhbWUtcGFyZW50OjE=",(...TRUNCATED) | {"contact_channel_mask":[[true,true,true,true,true,false,true],[true,true,true,true,true,false,true](...TRUNCATED) | {"sim_state":{"actors":{"cube":[[0.242095947265625,0.058322906494140625,0.01999995857477188,0.993474(...TRUNCATED) | {"outcome":{"reward":[4.9984540939331055,4.980624675750732,4.978454113006592,5.000920295715332,5.048(...TRUNCATED) | {
"action_index": 1,
"frame_index": 1
} | {"action_clipped":[false,true,true,true,true,true,true,true,false,true,true,true,true,true,true,true(...TRUNCATED) |
{"perturbation_from_source_controller_space":[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.12990379333496094(...TRUNCATED) | {"arm_target_qpos":[[-0.1744077354669571,0.8775786757469177,0.2471751719713211,-1.7217520475387573,-(...TRUNCATED) | {"branch_id":"ZXhwNl9mb3JtYWxfY29ycHVzX3YxOlBva2VDdWJlLXYxOnNlZWQtMTAwMDAwMDAwOnNhbWUtcGFyZW50OjI=",(...TRUNCATED) | {"contact_channel_mask":[[true,true,true,true,true,false,true],[true,true,true,true,true,false,true](...TRUNCATED) | {"sim_state":{"actors":{"cube":[[0.242095947265625,0.058322906494140625,0.01999995857477188,0.993474(...TRUNCATED) | {"outcome":{"reward":[5.204213619232178,5.16436243057251,5.186487197875977,5.213767051696777,5.32104(...TRUNCATED) | {
"action_index": 2,
"frame_index": 2
} | {"action_clipped":[false,true,true,true,true,true,true,true,false,true,true,true,true,true,true,true(...TRUNCATED) |
{"perturbation_from_source_controller_space":[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.14772117137908936(...TRUNCATED) | {"arm_target_qpos":[[-0.19594070315361023,0.9135603904724121,0.2827799618244171,-1.6809533834457397,(...TRUNCATED) | {"branch_id":"ZXhwNl9mb3JtYWxfY29ycHVzX3YxOlBva2VDdWJlLXYxOnNlZWQtMTAwMDAwMDAwOnNhbWUtcGFyZW50OjM=",(...TRUNCATED) | {"contact_channel_mask":[[true,true,true,true,true,false,true],[true,true,true,true,true,false,true](...TRUNCATED) | {"sim_state":{"actors":{"cube":[[0.242095947265625,0.058322906494140625,0.01999995857477188,0.993474(...TRUNCATED) | {"outcome":{"reward":[5.421520233154297,5.3408203125,5.419656753540039,5.441974639892578,5.624409198(...TRUNCATED) | {
"action_index": 3,
"frame_index": 3
} | {"action_clipped":[false,true,true,true,true,true,true,true,false,true,true,true,true,true,true,true(...TRUNCATED) |
{"perturbation_from_source_controller_space":[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.14772117137908936(...TRUNCATED) | {"arm_target_qpos":[[-0.21733224391937256,0.9507030844688416,0.3184362053871155,-1.6406856775283813,(...TRUNCATED) | {"branch_id":"ZXhwNl9mb3JtYWxfY29ycHVzX3YxOlBva2VDdWJlLXYxOnNlZWQtMTAwMDAwMDAwOnNhbWUtcGFyZW50OjQ=",(...TRUNCATED) | {"contact_channel_mask":[[true,true,true,true,true,false,true],[true,true,true,true,true,false,true](...TRUNCATED) | {"sim_state":{"actors":{"cube":[[0.242095947265625,0.058322906494140625,0.01999995857477188,0.993474(...TRUNCATED) | {"outcome":{"reward":[7.540605545043945,5.489900588989258,7.5308685302734375,7.53156852722168,5.7830(...TRUNCATED) | {
"action_index": 4,
"frame_index": 4
} | {"action_clipped":[false,true,true,true,true,true,true,true,false,true,true,true,true,true,true,true(...TRUNCATED) |
{"perturbation_from_source_controller_space":[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.12990379333496094(...TRUNCATED) | {"arm_target_qpos":[[-0.2386174499988556,0.9888696670532227,0.35406869649887085,-1.6009312868118286,(...TRUNCATED) | {"branch_id":"ZXhwNl9mb3JtYWxfY29ycHVzX3YxOlBva2VDdWJlLXYxOnNlZWQtMTAwMDAwMDAwOnNhbWUtcGFyZW50OjU=",(...TRUNCATED) | {"contact_channel_mask":[[true,true,true,true,true,false,true],[true,true,true,true,true,false,true](...TRUNCATED) | {"sim_state":{"actors":{"cube":[[0.24463653564453125,0.059173583984375,0.02004488930106163,0.9977477(...TRUNCATED) | {"outcome":{"reward":[4.973868370056152,5.590749740600586,5.097128868103027,4.940863609313965,5.5538(...TRUNCATED) | {
"action_index": 5,
"frame_index": 5
} | {"action_clipped":[false,true,true,true,true,true,true,true,false,true,true,true,true,true,true,true(...TRUNCATED) |
{"perturbation_from_source_controller_space":[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.09641814231872559(...TRUNCATED) | {"arm_target_qpos":[[-0.2598723769187927,1.028060793876648,0.3895597457885742,-1.5618958473205566,-0(...TRUNCATED) | {"branch_id":"ZXhwNl9mb3JtYWxfY29ycHVzX3YxOlBva2VDdWJlLXYxOnNlZWQtMTAwMDAwMDAwOnNhbWUtcGFyZW50OjY=",(...TRUNCATED) | {"contact_channel_mask":[[true,true,true,true,true,false,true],[true,true,true,true,true,false,true](...TRUNCATED) | {"sim_state":{"actors":{"cube":[[0.26161956787109375,0.06442642211914062,0.019999969750642776,0.9987(...TRUNCATED) | {"outcome":{"reward":[4.9146881103515625,5.657364845275879,4.921330451965332,4.90163516998291,5.3468(...TRUNCATED) | {
"action_index": 6,
"frame_index": 6
} | {"action_clipped":[false,true,true,true,true,true,true,true,false,true,true,true,true,true,true,true(...TRUNCATED) |
{"perturbation_from_source_controller_space":[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.05130302906036377(...TRUNCATED) | {"arm_target_qpos":[[-0.28095483779907227,1.0676649808883667,0.4251142144203186,-1.5228415727615356,(...TRUNCATED) | {"branch_id":"ZXhwNl9mb3JtYWxfY29ycHVzX3YxOlBva2VDdWJlLXYxOnNlZWQtMTAwMDAwMDAwOnNhbWUtcGFyZW50Ojc=",(...TRUNCATED) | {"contact_channel_mask":[[true,true,true,true,true,false,true],[true,true,true,true,true,false,true](...TRUNCATED) | {"sim_state":{"actors":{"cube":[[0.2722129821777344,0.06760787963867188,0.020000949501991272,0.99447(...TRUNCATED) | {"outcome":{"reward":[4.894362449645996,5.673391342163086,4.897189140319824,4.901792049407959,5.1873(...TRUNCATED) | {
"action_index": 7,
"frame_index": 7
} | {"action_clipped":[false,true,true,true,true,true,true,true,false,true,true,true,true,true,true,true(...TRUNCATED) |
{"perturbation_from_source_controller_space":[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.05130302906036377(...TRUNCATED) | {"arm_target_qpos":[[-0.06421245634555817,0.810269296169281,-0.10132793337106705,-1.794903039932251,(...TRUNCATED) | {"branch_id":"ZXhwNl9mb3JtYWxfY29ycHVzX3YxOlBva2VDdWJlLXYxOnNlZWQtMTAwMDA4MTkyOnNhbWUtcGFyZW50OjA=",(...TRUNCATED) | {"contact_channel_mask":[[true,true,true,true,true,false,true],[true,true,true,true,true,false,true](...TRUNCATED) | {"sim_state":{"actors":{"cube":[[0.2584190368652344,-0.08559036254882812,0.0199997927993536,0.972231(...TRUNCATED) | {"outcome":{"reward":[4.633967399597168,4.632575035095215,4.6381916999816895,4.6354660987854,4.63485(...TRUNCATED) | {
"action_index": 0,
"frame_index": 0
} | {"action_clipped":[false,true,true,false,true,true,true,true,false,false,true,false,true,true,true,t(...TRUNCATED) |
{"perturbation_from_source_controller_space":[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.09641814231872559(...TRUNCATED) | {"arm_target_qpos":[[-0.06816546618938446,0.8451490998268127,-0.1089993566274643,-1.7591252326965332(...TRUNCATED) | {"branch_id":"ZXhwNl9mb3JtYWxfY29ycHVzX3YxOlBva2VDdWJlLXYxOnNlZWQtMTAwMDA4MTkyOnNhbWUtcGFyZW50OjE=",(...TRUNCATED) | {"contact_channel_mask":[[true,true,true,true,true,false,true],[true,true,true,true,true,false,true](...TRUNCATED) | {"sim_state":{"actors":{"cube":[[0.2584190368652344,-0.08559036254882812,0.0199997927993536,0.972232(...TRUNCATED) | {"outcome":{"reward":[4.800292015075684,4.810328960418701,4.7893500328063965,4.8112335205078125,4.79(...TRUNCATED) | {
"action_index": 1,
"frame_index": 1
} | {"action_clipped":[false,true,true,false,true,true,true,true,false,false,true,true,true,true,true,tr(...TRUNCATED) |
End of preview.
PAP pretraining corpus
Interaction corpus (about 545M transitions) used to pretrain the PAP backbone. Part of the Physical Action Primitives collection. Code: https://github.com/Jinjaguo/physical-action-primitives.
data/corpus/fresh/<task>/shard_XXXX/ # fresh episodes (tier-1 packed episodes, teacher diagnostics, window index)
data/corpus/fresh_extension/<task>/shard_XXXX/ # scale extension of the fresh collection
data/corpus/branched/<task>/shard_XXXX/ # eight-step branches from shared parent states
data/corpus/historical/<wave>/<task>/ # historical exploration supplement
data/manifests/ # release indices, task split, normalization
checkpoints/teachers/<task>/ # PPO teacher policies used for collection
Shards are HDF5 files; all paths in the manifests are relative to $PAP_ROOT. Total size is about 267 GB.
hf download jinjaguo/pap-corpus --repo-type dataset --local-dir $PAP_ROOT
Citation
@inproceedings{guo2027pap,
title = {Beyond Verbs and Trajectories: Physical Action Primitives through Local Interaction Laws},
author = {Guo, Jinjia and Wang, Xinyu and Sun, Bowen and Zuo, Dongqi and Du, Jing},
year = {2027}
}
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