Code-as-Memory
Collection
Self-improving Robot Memory via Coding Agents • 7 items • Updated
Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ArrowInvalid
Message: JSON parse error: Missing a name for object member. in row 0
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 342, 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 677, in _extract_index
raise ValueError("All arrays must be of the same length")
ValueError: All arrays must be of the same length
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 244, 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 4645, 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 2866, in _head
return next(iter(self.iter(batch_size=n)))
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3073, in iter
for key, pa_table in ex_iterable.iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2581, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 345, in _generate_tables
raise e
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 291, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Missing a name for object member. in row 0Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
CaM development logs
Development evidence for Code-as-Memory Robot Memory programs: exact prompts, agent transcripts, tool logs, code revisions, frozen policies and generated analysis files, including images. Publishing generates no new simulator data.
MIKASA is organized into two archives: all 90 benchmark tasks, and the separate five-task VLA comparison with all four settings. The latter combines the previous three-setting bundle and separate up-to-250-demo bundle.
| Archive | Development policies | Settings | Compressed GiB |
|---|---|---|---|
| mikasa_robo | 90 | 50 demos + 50 training attempts | 9.59 |
| mikasa_robo_vla_suite | 20 | 25/50/up-to-250 demos only; 50 demos + 50 training attempts | 1.51 |
| rmbench | 20 | 25/50 demos only | 0.23 |
| robomme | 16 | 25 demos only | 0.23 |
| robomemarena | 78 | 25/50 demos only; demos + interaction | 4.38 |
The five comparison tasks are ShellGameTouch, InterceptMedium and RememberColor3/5/9
(-VLA-v0). Actual up-to-250 assignments are 237/56/250/250/246 successful RGB-D
replays; demo-only permits zero simulator attempts. Interaction budgets and exact
demo identities remain recorded per run, including RoboMemArena's task shortfalls.
Contents and integrity
The imported 80-task MIKASA transfer was verified against its file hashes and ZIP CRCs. All 90 full-benchmark frozen policies and all 20 VLA policies match the released executable code and program hashes. Original file bytes and historical paths inside logs are preserved. Identical contents may use internal tar hardlinks; no external data mounts or simulator assets are followed. These are evidence archives; use current CaM commands for fresh development.
The full MIKASA package follows the supplied development-only transfer: raw rollout
H5s, videos and action arrays are omitted, while generated analysis arrays/images
remain. The combined VLA package preserves both previously published payloads,
including generated held-out H5s/videos from the up-to-250 arm. Its original three
settings omit observation H5s. Other benchmark archives are unchanged. Exact scope,
file hashes and exclusions are recorded in manifests/.
Download evaluation logs and demonstration packages separately. No demonstration pool or external simulator asset is included here. Upstream benchmark code retains its notices and licenses.
Download and extract
Install zstd and the Hugging Face CLI:
hf download Yinpei/CaM_development_logs --repo-type dataset --local-dir CaM_development_logs
cd CaM_development_logs
sha256sum -c SHA256SUMS
mkdir -p ../public_runs/dev_logs
for archive in archives/*.tar.zst; do
tar --zstd -xf "$archive" -C ../public_runs/dev_logs
done
The MIKASA archives extract to mikasa_robo/demo_interaction_50/ and
mikasa_robo_vla_suite/{demo_only_25,demo_only_50,demo_only_250,demo_interaction_50}/.
For the consolidated MIKASA exports, verify individual files using
sha256sum -c ARCHIVE_SHA256SUMS inside each benchmark directory. Older in-run
manifests remain historical records of their original exports.
See Code-as-Memory for guides and policies.
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