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migration: extract is_so_robot_type and encoding_from_bounds helpers

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Split classify() into reusable pieces: is_so_robot_type() for the robot_type
name test, and encoding_from_bounds() as the single source of truth for the
degrees_old/degrees_new/normalized/radians decision from per-joint min/max
(layout-agnostic, so v2.1 episodes_stats and v3.0 stats.json both feed it).
This commit is contained in:
CarolinePascal
2026-07-17 15:32:52 +02:00
parent 0ec7c912e7
commit 425470759d
1 changed files with 33 additions and 18 deletions
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community_v3_migration/classify.py
+33 -18
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@@ -18,6 +18,11 @@ DEG_MIN = 105.0 # |val| above this => old-convention degrees
SAT_ATOL = 0.5 # closeness to +/-100 / 0 / 100 counted as normalization saturation
def is_so_robot_type(rt: str) -> bool:
"""True if the recorded ``robot_type`` denotes an in-scope SO-100/101 arm."""
return bool(rt) and (rt.startswith(SO_PREFIXES) or rt in SO_EXACT) and rt not in NEVER_FIX
def load_info(root: Path) -> dict:
return json.loads((Path(root) / "meta" / "info.json").read_text())
@@ -40,23 +45,14 @@ def _global_bounds(root: Path):
return lo, hi, key_used
def classify(root) -> dict:
root = Path(root)
info = load_info(root)
rt = info.get("robot_type", "") or ""
dim = (info.get("features", {}).get("action", {}).get("shape") or [None])[0]
out = {"root": str(root), "robot_type": rt, "action_dim": dim,
"codebase_version": info.get("codebase_version"), "ambiguous": False}
is_so = (rt.startswith(SO_PREFIXES) or rt in SO_EXACT) and rt not in NEVER_FIX
if not is_so:
return {**out, "is_so": False, "encoding": "non_so"}
lo, hi, key_used = _global_bounds(root)
if lo is None:
return {**out, "is_so": True, "encoding": "unknown", "ambiguous": True,
"note": "no action/state stats found"}
def encoding_from_bounds(lo, hi, rt: str) -> dict:
"""Detect the SO-arm joint encoding from per-joint global min/max and the robot_type name.
Layout-agnostic (v2.1 episodes_stats or v3.0 stats.json both reduce to lo/hi here), so it is
the single source of truth for the degrees_old / degrees_new / normalized / radians decision.
"""
lo = np.asarray(lo, dtype=float)
hi = np.asarray(hi, dtype=float)
maxabs = float(np.nanmax(np.abs(np.concatenate([lo, hi]))))
# saturation on any arm joint (index != gripper) at +/-100, or gripper at 0/100
n = 6
@@ -80,5 +76,24 @@ def classify(root) -> dict:
name_says_new = rt.endswith(("_follower", "_bimanual"))
ambiguous = (enc == "degrees_old" and name_says_new) or (enc in ("normalized", "degrees_new") and not name_says_new)
return {**out, "is_so": True, "encoding": enc, "maxabs": round(maxabs, 2),
"saturates": sat, "stats_key": key_used, "ambiguous": ambiguous}
return {"encoding": enc, "maxabs": round(maxabs, 2), "saturates": sat, "ambiguous": ambiguous}
def classify(root) -> dict:
root = Path(root)
info = load_info(root)
rt = info.get("robot_type", "") or ""
dim = (info.get("features", {}).get("action", {}).get("shape") or [None])[0]
out = {"root": str(root), "robot_type": rt, "action_dim": dim,
"codebase_version": info.get("codebase_version"), "ambiguous": False}
is_so = is_so_robot_type(rt)
if not is_so:
return {**out, "is_so": False, "encoding": "non_so"}
lo, hi, key_used = _global_bounds(root)
if lo is None:
return {**out, "is_so": True, "encoding": "unknown", "ambiguous": True,
"note": "no action/state stats found"}
return {**out, "is_so": True, "stats_key": key_used, **encoding_from_bounds(lo, hi, rt)}