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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'test' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: The document is empty.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
                  df = pandas_read_json(f)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                         ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1014, in read
                  obj = self._get_object_parser(self.data)
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1176, in parse
                  self._parse()
                  ~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1391, in _parse
                  self.obj = DataFrame(
                             ~~~~~~~~~^
                      ujson_loads(json, precise_float=self.precise_float), dtype=None
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/pandas/core/frame.py", line 782, in __init__
                  mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
                File "/usr/local/lib/python3.14/site-packages/pandas/core/internals/construction.py", line 503, in dict_to_mgr
                  return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)
                File "/usr/local/lib/python3.14/site-packages/pandas/core/internals/construction.py", line 114, in arrays_to_mgr
                  index = _extract_index(arrays)
                File "/usr/local/lib/python3.14/site-packages/pandas/core/internals/construction.py", line 680, in _extract_index
                  raise ValueError(
                      "Mixing dicts with non-Series may lead to ambiguous ordering."
                  )
              ValueError: Mixing dicts with non-Series may lead to ambiguous ordering.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 249, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4379, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2661, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2839, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 327, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                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.ArrowInvalid: JSON parse error: The document is empty.

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RadeonVLA-Reflex evaluation videos

This repository is the public video-and-evidence companion for RadeonVLA-Reflex, a Track 3 Physical AI submission by VisioBot Lab. It keeps videos beside the machine-readable results and hashes used to describe them.

Linked releases

What is included

Path Scope
videos/policy_success_20k/ Release-verified 20K first-attempt successes for banana and lemon
videos/policy_success_20k_world/ Single-panel derivatives of both verified 20K policy successes
videos/data_collection/ Successful strict-physics Physical-2K collection episodes for apple, banana, and plum
videos/task_success_world/ One certified Physical-2K success for each of the 20 fruit-and-destination tasks
videos/walkthrough/radeonvla_reflex_3min.mp4 200-second English walkthrough with bilingual captions and successful footage only
evidence/policy_success_20k/banana/ Standard one-episode JSON, CSV, and summary for scene/episode seed 53001
evidence/policy_success_20k/lemon/ Replay-probe JSON with scene seed 54000 and episode seed 54006 recorded separately
evidence/formal100/ Formal 100-rollout JSON and CSV evidence
evidence/world_success_examples.json Task, episode, seed, source timestamps, checksums, and certificate provenance for all 20 examples
SHA256SUMS Content hashes for every uploaded payload file

Formal result boundary

The released 100-rollout benchmark contains 20 tasks × 5 disjoint seeds:

  • Learned-policy first-attempt success: 36/100
  • Explicit strict-physics Precision-Reflex contribution: +55/100
  • Final system success: 91/100
  • P50 / P95 policy inference latency: 4.54 / 37.27 ms

The representative clips are a replay library, not the benchmark denominator. Aggregate results remain tied to evidence/formal100/evaluation.json and its CSV companion. The twenty clips under task_success_world/ are certified Physical-2K collection trajectories; they provide one successful visual example for every task but are not presented as policy-evaluation rollouts.

Video acceptance boundary

Every policy video in this repository passes a release-specific gate: the target fruit is inside the requested bowl, both the commanded and measured gripper positions are open, and the result remains valid after two additional seconds of physical simulation. The banana replay is a standard fixed-seed evaluation. The lemon replay records its fixed scene seed (54000) and episode seed (54006) separately because the scene was initialized once for that evaluation batch. Collection clips likewise show the full release and settled ending. The 20-task manifest binds each clip to its strict-physics certificate, zero kinematic interventions, exact source interval, and SHA256. Object pose teleportation, grasp glue, and post-hoc placement correction remain forbidden.

Language

Commands use the exact collected-language instruction source. The long walkthrough uses English narration and bilingual English/Chinese captions.

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