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Datasets:
williamTLmiller
/
matrixnorm-roboticsgraph-droid-sample

Tasks:
Robotics
Size:
10K<n<100K
Tags:
robotics
manipulation
lerobot
droid
physical-intelligence
imitation-learning
License:
Dataset card Data Studio Files
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The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
task_id: string
schema_id: string
eval_task_type: string
source_id: string
episode_id: string
expected_output: struct<success: bool, outcome_type: string>
  child 0, success: bool
  child 1, outcome_type: string
split: string
outcome: null
verification: struct<status: string, method: string, evidence: list<item: null>>
  child 0, status: string
  child 1, method: string
  child 2, evidence: list<item: null>
      child 0, item: null
task_type: string
confidence: double
timestep_id: string
input: struct<instruction: string, observation_refs: list<item: struct<type: string, camera: string, file:  (... 250 chars omitted)
  child 0, instruction: string
  child 1, observation_refs: list<item: struct<type: string, camera: string, file: string, episode_from_timestamp: double, episod (... 49 chars omitted)
      child 0, item: struct<type: string, camera: string, file: string, episode_from_timestamp: double, episode_to_timest (... 37 chars omitted)
          child 0, type: string
          child 1, camera: string
          child 2, file: string
          child 3, episode_from_timestamp: double
          child 4, episode_to_timestamp: double
          child 5, frame_timestamp: double
  child 2, robot_state: struct<state: list<item: double>, end_effector_pose: list<item: double>, state_layout: string, gripp (... 20 chars omitted)
      child 0, state: list<item: double>
          child 0, item: double
      child 1, end_effector_pose: list<item: double>
          child 0, item: double
      child 2, state_layout: string
      child 3, gripper_position: double
  child 3, task_goal: string
rights_status: string
quality_flags: list<item: null>
  child 0, item: null
to
{'task_id': Value('string'), 'schema_id': Value('string'), 'task_type': Value('string'), 'source_id': Value('string'), 'episode_id': Value('string'), 'timestep_id': Value('string'), 'input': {'instruction': Value('string'), 'observation_refs': List({'type': Value('string'), 'camera': Value('string'), 'file': Value('string'), 'episode_from_timestamp': Value('float64'), 'episode_to_timestamp': Value('float64'), 'frame_timestamp': Value('float64')}), 'robot_state': {'state': List(Value('float64')), 'end_effector_pose': List(Value('float64')), 'state_layout': Value('string'), 'gripper_position': Value('float64')}, 'task_goal': Value('string')}, 'expected_output': {'action_type': Value('string'), 'action_vector': List(Value('float64'))}, 'outcome': Json(decode=True), 'verification': {'status': Value('string'), 'method': Value('string'), 'evidence': List(Value('null'))}, 'confidence': Value('float64'), 'quality_flags': List(Value('null')), 'rights_status': Value('string'), 'split': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, 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 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              task_id: string
              schema_id: string
              eval_task_type: string
              source_id: string
              episode_id: string
              expected_output: struct<success: bool, outcome_type: string>
                child 0, success: bool
                child 1, outcome_type: string
              split: string
              outcome: null
              verification: struct<status: string, method: string, evidence: list<item: null>>
                child 0, status: string
                child 1, method: string
                child 2, evidence: list<item: null>
                    child 0, item: null
              task_type: string
              confidence: double
              timestep_id: string
              input: struct<instruction: string, observation_refs: list<item: struct<type: string, camera: string, file:  (... 250 chars omitted)
                child 0, instruction: string
                child 1, observation_refs: list<item: struct<type: string, camera: string, file: string, episode_from_timestamp: double, episod (... 49 chars omitted)
                    child 0, item: struct<type: string, camera: string, file: string, episode_from_timestamp: double, episode_to_timest (... 37 chars omitted)
                        child 0, type: string
                        child 1, camera: string
                        child 2, file: string
                        child 3, episode_from_timestamp: double
                        child 4, episode_to_timestamp: double
                        child 5, frame_timestamp: double
                child 2, robot_state: struct<state: list<item: double>, end_effector_pose: list<item: double>, state_layout: string, gripp (... 20 chars omitted)
                    child 0, state: list<item: double>
                        child 0, item: double
                    child 1, end_effector_pose: list<item: double>
                        child 0, item: double
                    child 2, state_layout: string
                    child 3, gripper_position: double
                child 3, task_goal: string
              rights_status: string
              quality_flags: list<item: null>
                child 0, item: null
              to
              {'task_id': Value('string'), 'schema_id': Value('string'), 'task_type': Value('string'), 'source_id': Value('string'), 'episode_id': Value('string'), 'timestep_id': Value('string'), 'input': {'instruction': Value('string'), 'observation_refs': List({'type': Value('string'), 'camera': Value('string'), 'file': Value('string'), 'episode_from_timestamp': Value('float64'), 'episode_to_timestamp': Value('float64'), 'frame_timestamp': Value('float64')}), 'robot_state': {'state': List(Value('float64')), 'end_effector_pose': List(Value('float64')), 'state_layout': Value('string'), 'gripper_position': Value('float64')}, 'task_goal': Value('string')}, 'expected_output': {'action_type': Value('string'), 'action_vector': List(Value('float64'))}, 'outcome': Json(decode=True), 'verification': {'status': Value('string'), 'method': Value('string'), 'evidence': List(Value('null'))}, 'confidence': Value('float64'), 'quality_flags': List(Value('null')), 'rights_status': Value('string'), 'split': Value('string')}
              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 1683, 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 1869, 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

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task_id
string
schema_id
string
task_type
string
source_id
string
episode_id
string
timestep_id
string
input
dict
expected_output
dict
outcome
unknown
verification
dict
confidence
float64
quality_flags
list
rights_status
string
split
string
d11629041f9a720d8e4b0a41:act
roboticsgraph
observation_to_action
droid_lerobot
d62b4a036bcd84c3ecde0a09
d11629041f9a720d8e4b0a41
{ "instruction": "", "observation_refs": [ { "type": "video", "camera": "exterior_1_left", "file": "videos/observation.images.exterior_1_left/chunk-000/file-000.mp4", "episode_from_timestamp": 0, "episode_to_timestamp": 11.1333333333, "frame_timestamp": 0 }, { "...
{ "action_type": "continuous", "action_vector": [ -0.2201297879, -0.4209160805, -0.1341398656, -2.3519082069, -0.1984667927, 2.2142367363, 0.0276581272, 0 ] }
{}
{ "status": "unverified", "method": "", "evidence": [] }
1
[]
open_license_reviewed
train
13bee241b0efeeea15561845:act
roboticsgraph
observation_to_action
droid_lerobot
d62b4a036bcd84c3ecde0a09
13bee241b0efeeea15561845
{ "instruction": "", "observation_refs": [ { "type": "video", "camera": "exterior_1_left", "file": "videos/observation.images.exterior_1_left/chunk-000/file-000.mp4", "episode_from_timestamp": 0, "episode_to_timestamp": 11.1333333333, "frame_timestamp": 0.0666666701 }, ...
{ "action_type": "continuous", "action_vector": [ -0.2208068818, -0.4209378362, -0.135385111, -2.3519101143, -0.1983821094, 2.2142517567, 0.0275954064, 0 ] }
{}
{ "status": "unverified", "method": "", "evidence": [] }
1
[]
open_license_reviewed
train
490dd6baca8c4811585db83d:act
roboticsgraph
observation_to_action
droid_lerobot
d62b4a036bcd84c3ecde0a09
490dd6baca8c4811585db83d
{ "instruction": "", "observation_refs": [ { "type": "video", "camera": "exterior_1_left", "file": "videos/observation.images.exterior_1_left/chunk-000/file-000.mp4", "episode_from_timestamp": 0, "episode_to_timestamp": 11.1333333333, "frame_timestamp": 0.1333333403 }, ...
{ "action_type": "continuous", "action_vector": [ -0.2210274339, -0.4209254682, -0.1363811344, -2.3519175053, -0.1982698143, 2.214266777, 0.0275134388, 0 ] }
{}
{ "status": "unverified", "method": "", "evidence": [] }
1
[]
open_license_reviewed
train
c07455e387e715b29e08a2ac:act
roboticsgraph
observation_to_action
droid_lerobot
d62b4a036bcd84c3ecde0a09
c07455e387e715b29e08a2ac
{ "instruction": "", "observation_refs": [ { "type": "video", "camera": "exterior_1_left", "file": "videos/observation.images.exterior_1_left/chunk-000/file-000.mp4", "episode_from_timestamp": 0, "episode_to_timestamp": 11.1333333333, "frame_timestamp": 0.200000003 }, ...
{ "action_type": "continuous", "action_vector": [ -0.2211027741, -0.420904845, -0.1367263347, -2.3519189358, -0.1982332468, 2.2142715454, 0.0274869911, 0 ] }
{}
{ "status": "unverified", "method": "", "evidence": [] }
1
[]
open_license_reviewed
train
24d28a7bcb01d6a9f31ce057:act
roboticsgraph
observation_to_action
droid_lerobot
d62b4a036bcd84c3ecde0a09
24d28a7bcb01d6a9f31ce057
{ "instruction": "", "observation_refs": [ { "type": "video", "camera": "exterior_1_left", "file": "videos/observation.images.exterior_1_left/chunk-000/file-000.mp4", "episode_from_timestamp": 0, "episode_to_timestamp": 11.1333333333, "frame_timestamp": 0.2666666806 }, ...
{ "action_type": "continuous", "action_vector": [ -0.2210712135, -0.4209221601, -0.1367033273, -2.3519172668, -0.1982334852, 2.2142753601, 0.0274877213, 0 ] }
{}
{ "status": "unverified", "method": "", "evidence": [] }
1
[]
open_license_reviewed
train
149da75e96fc74a031605468:act
roboticsgraph
observation_to_action
droid_lerobot
d62b4a036bcd84c3ecde0a09
149da75e96fc74a031605468
{ "instruction": "", "observation_refs": [ { "type": "video", "camera": "exterior_1_left", "file": "videos/observation.images.exterior_1_left/chunk-000/file-000.mp4", "episode_from_timestamp": 0, "episode_to_timestamp": 11.1333333333, "frame_timestamp": 0.3333333433 }, ...
{ "action_type": "continuous", "action_vector": [ -0.2211121023, -0.4209241867, -0.1367889792, -2.3519251347, -0.1982260197, 2.2142796516, 0.0274799783, 0 ] }
{}
{ "status": "unverified", "method": "", "evidence": [] }
1
[]
open_license_reviewed
train
6d480e33608f31bd67da3113:act
roboticsgraph
observation_to_action
droid_lerobot
d62b4a036bcd84c3ecde0a09
6d480e33608f31bd67da3113
{ "instruction": "", "observation_refs": [ { "type": "video", "camera": "exterior_1_left", "file": "videos/observation.images.exterior_1_left/chunk-000/file-000.mp4", "episode_from_timestamp": 0, "episode_to_timestamp": 11.1333333333, "frame_timestamp": 0.400000006 }, ...
{ "action_type": "continuous", "action_vector": [ -0.221106559, -0.4209115803, -0.1367944777, -2.3519232273, -0.1982244253, 2.2142765522, 0.0274812635, 0 ] }
{}
{ "status": "unverified", "method": "", "evidence": [] }
1
[]
open_license_reviewed
train
c3be05bb10107bf75aa2051a:act
roboticsgraph
observation_to_action
droid_lerobot
d62b4a036bcd84c3ecde0a09
c3be05bb10107bf75aa2051a
{ "instruction": "", "observation_refs": [ { "type": "video", "camera": "exterior_1_left", "file": "videos/observation.images.exterior_1_left/chunk-000/file-000.mp4", "episode_from_timestamp": 0, "episode_to_timestamp": 11.1333333333, "frame_timestamp": 0.4666666687 }, ...
{ "action_type": "continuous", "action_vector": [ -0.2211101949, -0.4209137261, -0.1368037313, -2.3519201279, -0.1982206553, 2.2142755985, 0.0274769086, 0 ] }
{}
{ "status": "unverified", "method": "", "evidence": [] }
1
[]
open_license_reviewed
train
7669b178fcfcab3862f0da52:act
roboticsgraph
observation_to_action
droid_lerobot
d62b4a036bcd84c3ecde0a09
7669b178fcfcab3862f0da52
{ "instruction": "", "observation_refs": [ { "type": "video", "camera": "exterior_1_left", "file": "videos/observation.images.exterior_1_left/chunk-000/file-000.mp4", "episode_from_timestamp": 0, "episode_to_timestamp": 11.1333333333, "frame_timestamp": 0.5333333611 }, ...
{ "action_type": "continuous", "action_vector": [ -0.2211081088, -0.4209182858, -0.1368003339, -2.3519165516, -0.1982204616, 2.2142755985, 0.0274808481, 0 ] }
{}
{ "status": "unverified", "method": "", "evidence": [] }
1
[]
open_license_reviewed
train
d2b5ee6555ceb7a0e29d1d12:act
roboticsgraph
observation_to_action
droid_lerobot
d62b4a036bcd84c3ecde0a09
d2b5ee6555ceb7a0e29d1d12
{ "instruction": "", "observation_refs": [ { "type": "video", "camera": "exterior_1_left", "file": "videos/observation.images.exterior_1_left/chunk-000/file-000.mp4", "episode_from_timestamp": 0, "episode_to_timestamp": 11.1333333333, "frame_timestamp": 0.6000000238 }, ...
{ "action_type": "continuous", "action_vector": [ -0.2211101204, -0.4209163785, -0.1368122697, -2.3519151211, -0.1982200742, 2.2142777443, 0.0274746493, 0 ] }
{}
{ "status": "unverified", "method": "", "evidence": [] }
1
[]
open_license_reviewed
train
24b9eebd012caf36047472c4:act
roboticsgraph
observation_to_action
droid_lerobot
d62b4a036bcd84c3ecde0a09
24b9eebd012caf36047472c4
{ "instruction": "", "observation_refs": [ { "type": "video", "camera": "exterior_1_left", "file": "videos/observation.images.exterior_1_left/chunk-000/file-000.mp4", "episode_from_timestamp": 0, "episode_to_timestamp": 11.1333333333, "frame_timestamp": 0.6666666865 }, ...
{ "action_type": "continuous", "action_vector": [ -0.2211149633, -0.4209143221, -0.1368190199, -2.3519225121, -0.1982178241, 2.2142746449, 0.0274809599, 0 ] }
{}
{ "status": "unverified", "method": "", "evidence": [] }
1
[]
open_license_reviewed
train
4b6d23796c6c35586da06739:act
roboticsgraph
observation_to_action
droid_lerobot
d62b4a036bcd84c3ecde0a09
4b6d23796c6c35586da06739
{ "instruction": "", "observation_refs": [ { "type": "video", "camera": "exterior_1_left", "file": "videos/observation.images.exterior_1_left/chunk-000/file-000.mp4", "episode_from_timestamp": 0, "episode_to_timestamp": 11.1333333333, "frame_timestamp": 0.7333333492 }, ...
{ "action_type": "continuous", "action_vector": [ -0.2211076915, -0.4209160805, -0.1368085444, -2.3519160748, -0.1982192248, 2.2142779827, 0.0274770055, 0 ] }
{}
{ "status": "unverified", "method": "", "evidence": [] }
1
[]
open_license_reviewed
train
8b426c27d5f5bd9424057e98:act
roboticsgraph
observation_to_action
droid_lerobot
d62b4a036bcd84c3ecde0a09
8b426c27d5f5bd9424057e98
{ "instruction": "", "observation_refs": [ { "type": "video", "camera": "exterior_1_left", "file": "videos/observation.images.exterior_1_left/chunk-000/file-000.mp4", "episode_from_timestamp": 0, "episode_to_timestamp": 11.1333333333, "frame_timestamp": 0.8000000119 }, ...
{ "action_type": "continuous", "action_vector": [ -0.2211129367, -0.420910269, -0.1368185431, -2.3519184589, -0.198221609, 2.2142746449, 0.0274773706, 0 ] }
{}
{ "status": "unverified", "method": "", "evidence": [] }
1
[]
open_license_reviewed
train
803b42be78f0ff224008a1de:act
roboticsgraph
observation_to_action
droid_lerobot
d62b4a036bcd84c3ecde0a09
803b42be78f0ff224008a1de
{ "instruction": "", "observation_refs": [ { "type": "video", "camera": "exterior_1_left", "file": "videos/observation.images.exterior_1_left/chunk-000/file-000.mp4", "episode_from_timestamp": 0, "episode_to_timestamp": 11.1333333333, "frame_timestamp": 0.8666666746 }, ...
{ "action_type": "continuous", "action_vector": [ -0.2211131752, -0.4209193289, -0.1368184984, -2.3519182205, -0.1982190013, 2.2142779827, 0.0274768751, 0 ] }
{}
{ "status": "unverified", "method": "", "evidence": [] }
1
[]
open_license_reviewed
train
976a839c2aad8579d6906277:act
roboticsgraph
observation_to_action
droid_lerobot
d62b4a036bcd84c3ecde0a09
976a839c2aad8579d6906277
{ "instruction": "", "observation_refs": [ { "type": "video", "camera": "exterior_1_left", "file": "videos/observation.images.exterior_1_left/chunk-000/file-000.mp4", "episode_from_timestamp": 0, "episode_to_timestamp": 11.1333333333, "frame_timestamp": 0.9333333373 }, ...
{ "action_type": "continuous", "action_vector": [ -0.2211150974, -0.4209201336, -0.1368200779, -2.3519182205, -0.1982183903, 2.2142772675, 0.0274794064, 0 ] }
{}
{ "status": "unverified", "method": "", "evidence": [] }
1
[]
open_license_reviewed
train
3040ff7788050c2c43d2bfe2:act
roboticsgraph
observation_to_action
droid_lerobot
d62b4a036bcd84c3ecde0a09
3040ff7788050c2c43d2bfe2
{ "instruction": "", "observation_refs": [ { "type": "video", "camera": "exterior_1_left", "file": "videos/observation.images.exterior_1_left/chunk-000/file-000.mp4", "episode_from_timestamp": 0, "episode_to_timestamp": 11.1333333333, "frame_timestamp": 1 }, { "...
{ "action_type": "continuous", "action_vector": [ -0.2211141735, -0.4209153354, -0.1368166208, -2.3519184589, -0.1982175112, 2.2142755985, 0.0274813883, 0 ] }
{}
{ "status": "unverified", "method": "", "evidence": [] }
1
[]
open_license_reviewed
train
da47c059afb0d68f6bb0d49d:act
roboticsgraph
observation_to_action
droid_lerobot
d62b4a036bcd84c3ecde0a09
da47c059afb0d68f6bb0d49d
{ "instruction": "", "observation_refs": [ { "type": "video", "camera": "exterior_1_left", "file": "videos/observation.images.exterior_1_left/chunk-000/file-000.mp4", "episode_from_timestamp": 0, "episode_to_timestamp": 11.1333333333, "frame_timestamp": 1.0666667223 }, ...
{ "action_type": "continuous", "action_vector": [ -0.2211119235, -0.4209114909, -0.1368205547, -2.3519184589, -0.1982180923, 2.2142779827, 0.027476294, 0 ] }
{}
{ "status": "unverified", "method": "", "evidence": [] }
1
[]
open_license_reviewed
train
17b2f062b9d1fcfe4bbb226a:act
roboticsgraph
observation_to_action
droid_lerobot
d62b4a036bcd84c3ecde0a09
17b2f062b9d1fcfe4bbb226a
{ "instruction": "", "observation_refs": [ { "type": "video", "camera": "exterior_1_left", "file": "videos/observation.images.exterior_1_left/chunk-000/file-000.mp4", "episode_from_timestamp": 0, "episode_to_timestamp": 11.1333333333, "frame_timestamp": 1.1333333254 }, ...
{ "action_type": "continuous", "action_vector": [ -0.2211173177, -0.4209159315, -0.1368236244, -2.3519170284, -0.1982186586, 2.2142763138, 0.0274775978, 0 ] }
{}
{ "status": "unverified", "method": "", "evidence": [] }
1
[]
open_license_reviewed
train
028a6e205d87a38eee48627d:act
roboticsgraph
observation_to_action
droid_lerobot
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End of preview.

RoboticsGraph · DROID — FREE SAMPLE (327 episodes, videos included)

This is a free sample of a much larger commercial release. The full version — 1,700,000 frames / 5,819 episodes / 70 collection sites, with a 13.7M-edge relationship graph and full video linkage — is available for purchase. Contact: energea@energeatech.com.

MatrixNorm RoboticsGraph turns raw robot-learning corpora into training-ready, rights-tracked, leakage-safe datasets: the verified chain instruction → observation → state → action → outcome as structured rows — not raw robot video dumps.

What's in the sample

327 real-world Franka manipulation episodes (96,183 timesteps, 45 collection sites) — and every sample episode's exterior-camera video is included and frame-addressable from the data rows.

file what it is
roboticsgraph_dataset.jsonl / .csv THE flat normalized table — one row per timestep, 38 columns
training_tasks.jsonl.gz 96,183 training tasks: instruction + full robot_state → action vector
train.jsonl.gz / validation.jsonl.gz / test.jsonl.gz the same tasks, materialized by episode-grouped split (220/47/60 episodes — zero episode leakage)
eval_tasks.jsonl 327 held-out predict_success eval tasks
hin_edges.jsonl.gz 774,067-edge relationship graph (episode/timestep/state/action/outcome/task)
videos/ + videos_index.* the covering exterior-camera MP4 + per-episode/per-camera windows; video_frame_timestamp on every row seeks straight to the frame
source_manifest.json / rights_manifest.csv provenance, license trail, raw-archive SHA256 
from datasets import load_dataset
rows  = load_dataset("json", data_files="roboticsgraph_dataset.jsonl", split="train")
tasks = load_dataset("json", data_files="train.jsonl.gz", split="train")

Field coverage (this sample)

  • robot_embodiment (Franka, verified from source metadata): 100%
  • state / end-effector pose / gripper / action vectors: 100%
  • video linkage (file + exact frame timestamp per row): 100%
  • collection site + collector provenance: 100%
  • outcome (success/failure ground truth): 100%
  • language instruction: 72.8% — the remainder have no label anywhere in the source; they are flagged, never invented, and their tasks are typed observation_to_action. Alternate source phrasings are preserved.

Why this beats using the raw source directly

  1. Training-ready tasks — every timestep is already an (instruction, robot_state, video-frame ref) → action_vector pair; no parsing, joining, or episode reconstruction needed.
  2. Leakage-safe splits — episode-grouped, hash-materialized, auditable via split_group_key. No silent train/test contamination.
  3. Honesty contract — every label traces to a real source field; gaps are flagged, not filled. Two corrupt source columns were detected and excluded (documented below).
  4. Rights tracked end-to-end — apache-2.0 verified at the source, rights manifest + raw-archive SHA256 included.
  5. Relationship graph — HIN edges connect timesteps, states, actions, outcomes and tasks for graph-based learning and retrieval.

The full release (for sale)

  • 1,700,000 frames / 5,819 episodes / 70 sites (≈18× this sample)
  • 1.7M training tasks + 5,819 eval tasks, 13,682,009 HIN edges
  • full video index for all episodes × 3 cameras (exterior 1/2 + wrist)
  • same validated 100% coverage on embodiment/state/video/provenance fields
  • custom normalization runs (other robots, other corpora, up to the full 95,658-episode DROID) available on request

Contact: energea@energeatech.com

Honesty notes

  • The source mirror's task_category and date columns are corrupt duplicates of building and collector_id (verified) — excluded rather than shipped as fake labels.
  • gripper_action_state open/closed is a disclosed 0.5 threshold over the continuous gripper_position (preserved verbatim).
  • Instructions are never synthesized; unlabeled episodes stay unlabeled.

License

This packaged sample (the flat table, task construction, splits, HIN graph, video index, and this documentation) is licensed CC BY-NC 4.0 — attribution required, non-commercial use only. Commercial use requires a license — contact energea@energeatech.com (the full 1.7M-frame release is sold under a separate commercial license).

Scope note: this NC restriction covers williamTLmiller's normalization and packaging work. It does not and cannot restrict the underlying DROID recordings themselves, which remain separately available under Apache License 2.0 from their original source (lerobot/droid_1.0.1). See LICENSE (CC BY-NC 4.0, this package) and LICENSE-APACHE-2.0-UNDERLYING-DATA.txt (the source data's own license).

Attribution

Derived from DROID via the official LeRobot-format mirror lerobot/droid_1.0.1 (Apache-2.0). Please cite the DROID paper when using the underlying data.
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