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migration: relabel robot_type to 'unknown' when SO name doesn't match structure
is_so is decided purely from the robot_type string, so datasets labeled so100/so101 whose joint dim isn't a multiple of 6 carry a provably wrong label. Rewrite meta/info.json robot_type to 'unknown' in that case so the v3.0 output isn't misidentified as an SO arm.
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@@ -17,6 +17,13 @@ def _stack(col_values) -> np.ndarray:
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return np.stack([np.asarray(v, dtype=np.float64) for v in col_values]) # (N, D)
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def _set_robot_type(root: Path, robot_type: str) -> None:
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info_path = root / "meta" / "info.json"
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info = json.loads(info_path.read_text())
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info["robot_type"] = robot_type
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info_path.write_text(json.dumps(info, indent=4))
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def _rewrite_parquet(root: Path, encoding: str) -> None:
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for pq in sorted((root / "data").glob("*/*.parquet")):
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df = pd.read_parquet(pq)
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@@ -82,10 +89,12 @@ def fix_dataset_in_place(root) -> dict:
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dims = [feats[c]["shape"][0] for c in VALUE_COLS if c in feats and feats[c].get("shape")]
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if any(d % 6 != 0 for d in dims):
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# Not a plain stack of 6-joint SO arms (e.g. 7-dim with an appended EE pose): the
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# degrees mapping doesn't apply, so migrate structurally and leave the values alone.
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return {**cls, "converted": False,
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# degrees mapping doesn't apply. The `so100`/`so101` robot_type is provably wrong for
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# this structure, so relabel it 'unknown' and migrate structurally without touching values.
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_set_robot_type(root, "unknown")
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return {**cls, "robot_type": "unknown", "converted": False,
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"action": f"structural v2.1->v3.0 only; joint dims {dims} not a multiple of 6 "
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"(non-standard arm), joint values left unchanged"}
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"(non-standard arm), robot_type set to 'unknown', joint values left unchanged"}
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# drop stray files that would otherwise be uploaded
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for junk in (root / "meta").glob("info.json.bak"):
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junk.unlink()
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