Datasets:

protocolcompany
/

physics-verification-corpus

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
continuous_current_A: double
cryptographic_seal: string
failure_mode: string
max_cell_temp_C: double
static_payload_kg: double
survived: bool
trajectory_id: int64
ankle_roll_torque_Nm: double
max_zmp_deflection_m: double
edge_lateral_lever_m: double
to
{'ankle_roll_torque_Nm': Value('float64'), 'cryptographic_seal': Value('string'), 'edge_lateral_lever_m': Value('float64'), 'failure_mode': Value('string'), 'max_zmp_deflection_m': Value('float64'), 'survived': Value('bool'), 'trajectory_id': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1872, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 609, in wrapped
                  for item in generator(*args, **kwargs):
                              ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 265, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 120, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              continuous_current_A: double
              cryptographic_seal: string
              failure_mode: string
              max_cell_temp_C: double
              static_payload_kg: double
              survived: bool
              trajectory_id: int64
              ankle_roll_torque_Nm: double
              max_zmp_deflection_m: double
              edge_lateral_lever_m: double
              to
              {'ankle_roll_torque_Nm': Value('float64'), 'cryptographic_seal': Value('string'), 'edge_lateral_lever_m': Value('float64'), 'failure_mode': Value('string'), 'max_zmp_deflection_m': Value('float64'), 'survived': Value('bool'), 'trajectory_id': Value('int64')}
              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 1342, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 907, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1739, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1922, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

ankle_roll_torque_Nm float64	cryptographic_seal string	edge_lateral_lever_m float64	failure_mode string	max_zmp_deflection_m float64	survived bool	trajectory_id int64
9.49	sha256:1x_neo_ankle_inversion_0	0.028	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.596	false	0
5.5	sha256:1x_neo_ankle_inversion_1	0.012	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.424	false	1
7.69	sha256:1x_neo_ankle_inversion_2	0.02	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.588	false	2
5.79	sha256:1x_neo_ankle_inversion_3	0.013	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.382	false	3
13.56	sha256:1x_neo_ankle_inversion_4	0.034	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.914	false	4
10.81	sha256:1x_neo_ankle_inversion_5	0.031	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.752	false	5
5.66	sha256:1x_neo_ankle_inversion_6	0.016	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.429	false	6
5.48	sha256:1x_neo_ankle_inversion_7	0.013	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.385	false	7
4.99	sha256:1x_neo_ankle_inversion_8	0.015	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.383	false	8
4.28	sha256:1x_neo_ankle_inversion_9	0.013	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.322	false	9
15.71	sha256:1x_neo_ankle_inversion_10	0.04	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.929	false	10
3.49	sha256:1x_neo_ankle_inversion_11	0.009	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.257	false	11
15.91	sha256:1x_neo_ankle_inversion_12	0.035	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.893	false	12
6.01	sha256:1x_neo_ankle_inversion_13	0.014	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.434	false	13
3.63	sha256:1x_neo_ankle_inversion_14	0.01	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.275	false	14
10.98	sha256:1x_neo_ankle_inversion_15	0.023	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.732	false	15
6.82	sha256:1x_neo_ankle_inversion_16	0.019	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.455	false	16
4.3	sha256:1x_neo_ankle_inversion_17	0.013	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.306	false	17
2.46	sha256:1x_neo_ankle_inversion_18	0.006	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.195	false	18
3.37	sha256:1x_neo_ankle_inversion_19	0.008	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.226	false	19
4.19	sha256:1x_neo_ankle_inversion_20	0.009	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.289	false	20
1.97	sha256:1x_neo_ankle_inversion_21	0.004	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.14	false	21
2.96	sha256:1x_neo_ankle_inversion_22	0.008	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.204	false	22
13.38	sha256:1x_neo_ankle_inversion_23	0.034	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.782	false	23
7.58	sha256:1x_neo_ankle_inversion_24	0.022	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.519	false	24
12.44	sha256:1x_neo_ankle_inversion_25	0.03	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.819	false	25
7.42	sha256:1x_neo_ankle_inversion_26	0.019	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.512	false	26
3.4	sha256:1x_neo_ankle_inversion_27	0.007	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.245	false	27
14.74	sha256:1x_neo_ankle_inversion_28	0.039	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.963	false	28
9.93	sha256:1x_neo_ankle_inversion_29	0.024	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.719	false	29
4.44	sha256:1x_neo_ankle_inversion_30	0.011	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.348	false	30
1.65	sha256:1x_neo_ankle_inversion_31	0.004	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.114	false	31
14.09	sha256:1x_neo_ankle_inversion_32	0.031	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.95	false	32
4.98	sha256:1x_neo_ankle_inversion_33	0.013	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.37	false	33
13.36	sha256:1x_neo_ankle_inversion_34	0.03	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.879	false	34
4.56	sha256:1x_neo_ankle_inversion_35	0.011	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.306	false	35
3.59	sha256:1x_neo_ankle_inversion_36	0.01	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.262	false	36
9.31	sha256:1x_neo_ankle_inversion_37	0.02	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.627	false	37
3.48	sha256:1x_neo_ankle_inversion_38	0.008	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.25	false	38
10.88	sha256:1x_neo_ankle_inversion_39	0.03	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.707	false	39
3.92	sha256:1x_neo_ankle_inversion_40	0.009	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.268	false	40
9.54	sha256:1x_neo_ankle_inversion_41	0.02	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.698	false	41
5.51	sha256:1x_neo_ankle_inversion_42	0.011	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.399	false	42
15.74	sha256:1x_neo_ankle_inversion_43	0.035	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.94	false	43
15.2	sha256:1x_neo_ankle_inversion_44	0.032	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.954	false	44
2.88	sha256:1x_neo_ankle_inversion_45	0.007	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.197	false	45
3.42	sha256:1x_neo_ankle_inversion_46	0.008	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.237	false	46
12.37	sha256:1x_neo_ankle_inversion_47	0.03	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.778	false	47
16.7	sha256:1x_neo_ankle_inversion_48	0.036	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.953	false	48
5.4	sha256:1x_neo_ankle_inversion_49	0.015	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.354	false	49
6.08	sha256:1x_neo_ankle_inversion_50	0.015	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.446	false	50
17.08	sha256:1x_neo_ankle_inversion_51	0.038	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.915	false	51
4.25	sha256:1x_neo_ankle_inversion_52	0.012	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.291	false	52
15	sha256:1x_neo_ankle_inversion_53	0.038	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.913	false	53
12.77	sha256:1x_neo_ankle_inversion_54	0.035	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.766	false	54
2.41	sha256:1x_neo_ankle_inversion_55	0.006	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.191	false	55
17.33	sha256:1x_neo_ankle_inversion_56	0.038	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.984	false	56
7.03	sha256:1x_neo_ankle_inversion_57	0.02	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.528	false	57
5.14	sha256:1x_neo_ankle_inversion_58	0.014	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.398	false	58
3.4	sha256:1x_neo_ankle_inversion_59	0.009	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.227	false	59
11.5	sha256:1x_neo_ankle_inversion_60	0.032	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.745	false	60
17.45	sha256:1x_neo_ankle_inversion_61	0.037	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.973	false	61
8.81	sha256:1x_neo_ankle_inversion_62	0.019	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.637	false	62
16.41	sha256:1x_neo_ankle_inversion_63	0.04	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.942	false	63
4.92	sha256:1x_neo_ankle_inversion_64	0.012	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.366	false	64
1.99	sha256:1x_neo_ankle_inversion_65	0.005	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.159	false	65
7.01	sha256:1x_neo_ankle_inversion_66	0.015	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.505	false	66
13.06	sha256:1x_neo_ankle_inversion_67	0.038	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.804	false	67
7.87	sha256:1x_neo_ankle_inversion_68	0.02	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.558	false	68
7.9	sha256:1x_neo_ankle_inversion_69	0.018	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.572	false	69
8.96	sha256:1x_neo_ankle_inversion_70	0.023	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.563	false	70
15.92	sha256:1x_neo_ankle_inversion_71	0.04	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.894	false	71
9.51	sha256:1x_neo_ankle_inversion_72	0.027	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.605	false	72
5.91	sha256:1x_neo_ankle_inversion_73	0.015	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.386	false	73
11.36	sha256:1x_neo_ankle_inversion_74	0.025	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.824	false	74
10.68	sha256:1x_neo_ankle_inversion_75	0.028	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.7	false	75
15.32	sha256:1x_neo_ankle_inversion_76	0.035	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.866	false	76
14.51	sha256:1x_neo_ankle_inversion_77	0.036	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.931	false	77
3.41	sha256:1x_neo_ankle_inversion_78	0.009	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.244	false	78
9.47	sha256:1x_neo_ankle_inversion_79	0.027	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.704	false	79
4.18	sha256:1x_neo_ankle_inversion_80	0.009	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.328	false	80
15.27	sha256:1x_neo_ankle_inversion_81	0.037	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.972	false	81
17.58	sha256:1x_neo_ankle_inversion_82	0.039	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.97	false	82
10.51	sha256:1x_neo_ankle_inversion_83	0.029	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.666	false	83
9.31	sha256:1x_neo_ankle_inversion_84	0.027	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.664	false	84
18.88	sha256:1x_neo_ankle_inversion_85	0.04	E2E_UNMODELED_ANKLE_INVERSION_FALL	1.034	false	85
2.53	sha256:1x_neo_ankle_inversion_86	0.006	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.186	false	86
12.85	sha256:1x_neo_ankle_inversion_87	0.029	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.785	false	87
4.29	sha256:1x_neo_ankle_inversion_88	0.011	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.33	false	88
4.13	sha256:1x_neo_ankle_inversion_89	0.008	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.321	false	89
2.57	sha256:1x_neo_ankle_inversion_90	0.006	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.2	false	90
10.9	sha256:1x_neo_ankle_inversion_91	0.031	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.796	false	91
3.85	sha256:1x_neo_ankle_inversion_92	0.011	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.297	false	92
7.01	sha256:1x_neo_ankle_inversion_93	0.018	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.493	false	93
9.23	sha256:1x_neo_ankle_inversion_94	0.022	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.692	false	94
9.07	sha256:1x_neo_ankle_inversion_95	0.024	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.607	false	95
7.83	sha256:1x_neo_ankle_inversion_96	0.022	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.547	false	96
7.99	sha256:1x_neo_ankle_inversion_97	0.021	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.513	false	97
3.82	sha256:1x_neo_ankle_inversion_98	0.008	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.285	false	98
10.78	sha256:1x_neo_ankle_inversion_99	0.031	E2E_UNMODELED_ANKLE_INVERSION_FALL	0.748	false	99

End of preview.

G^G Physics Characterizations: Autonomous Systems Corpus

130+ trajectory datasets across 30 autonomous systems platforms. ~43 GB of sealed, deterministic physics.

Generated by G^G (genesis_core), a pure Rust physics engine built from first principles. No external physics libraries. Every equation traces to a published reference. Every trajectory is SHA-256 sealed from initial conditions to terminal state.

Each dataset takes the platform's published engineering specifications and runs the governing physics equations to characterize the operational envelope: where the physics holds, where it degrades, and where the failure boundary is.

Coverage

Robotics Platforms

Platform	Physics Domain	Key Finding
Boston Dynamics Atlas / Spot	Joint torsion, thermal dynamics, friction	Dynamic CG shift, friction collapse boundary
Figure AI Figure 01	Payload mechanics, battery physics, haptics	CG shift under payload, sensor saturation
Agility Robotics Digit	Digitigrade contact, elastomer physics	Toe-pad contact boundary, warehouse navigation
Skild AI general robotics	Morphology thermal, gear dynamics	Compute thermal load interaction
Shield AI V-BAT / Hivemind	Swarm VIO, IMU resonance, crosswind	Multi-agent odometry divergence
Anybotics ANYmal	Terrain adaptation, slip mechanics	Off-road slip boundary
Ghost Robotics Vision 60	Off-road dynamics, payload limits	Terrain contact limits
Unitree H1 / Go2	Locomotion physics, terrain response	Gait boundary on irregular terrain
Sanctuary AI Phoenix	Manipulation physics, proprioception	Dexterity envelope

Autonomous Vehicles and eVTOL

Platform	Physics Domain	Key Finding
Wayve end-to-end AV	Low-friction surface physics, brake dynamics	Vision boundary on ice and wet roads
Joby Aviation air taxi	Wake aerodynamics, thermal throttle, acoustics	Transition flight wake collapse
Archer Aviation Midnight	eVTOL rotor dynamics, battery thermal limits	Hover thermal ceiling
Aurora Innovation	Sensor fusion limits, navigation edge cases	GPS boundary conditions
Tesla FSD	Camera physics (Mie scattering), EKF limits	Fog camera range collapse
Anduril systems	Sensor fusion, VTOL transition physics	Mach transition boundary
Skydio autonomous drone	Visual odometry, atmospheric limits	Wind envelope for visual nav
Waabi self-driving truck	Highway physics, adverse weather	Weather-driven edge cases

Defense Platforms

Platform	Physics Domain	Key Finding
AeroVironment Juggernaut / Switchblade	Ground dynamics, terminal guidance	Vibration resonance, bearing limits
BAE Systems AMPV / EPAWSS	Suspension dynamics, EW physics	Resonance and thermal boundaries
Boeing MQ-25 / Orca / Apache	Wake turbulence, hydrodynamics	Turbulence coupling limits
General Dynamics Stryker / AbramsX	Sensor fusion, autoloader mechanics	Multipath and mechanical sync limits
Huntington Ingalls XLUUV / USV	Naval hydrodynamics, hull physics	Cavitation and slamming thresholds
Kratos XQ-58 / Mako	Aeroelastic dynamics, plasma physics	Flutter and plasma blackout boundaries
L3Harris RF / GPS systems	Signal coherence, gimbal dynamics	Signal mesh and resonance limits
Leidos autonomous systems	Multi-domain physics	Platform-specific envelope
Northrop Grumman	Aerospace and ground platforms	Autonomous wingman physics
Raytheon	Guidance and targeting	Terminal physics boundaries

Methodology

Published engineering specifications and physical parameters → first-principles physics (G^G genesis_core, pure Rust) → Monte Carlo integration at 1000 Hz → SHA-256 sealed output.

Every trajectory is independently reproducible. Same seed → same trajectory → same SHA-256 hash, on any machine. The complete proof chain is available for verification.

Full Thesis

No Way Back: Crossing the Physics Gap in Physical AI — 49-page technical paper documenting the findings from this corpus and thirteen physics substrates. Every finding cryptographically anchored.

aijesusbro.com | kruze@aijesusbro.com

Downloads last month: 260

Total file size:

43.3 GB