Warn and rescue sysid parameters that start at a box bound.

ParameterDict.move_off_bounds() shifts at-bound values into the interior;
optimize() warns when any non-frozen component starts at a bound.
This commit is contained in:
Kevin Zakka
2026-05-25 20:30:33 -07:00
parent 66156c7d9a
commit 5ae677f026
3 changed files with 75 additions and 0 deletions
+18
View File
@@ -179,6 +179,24 @@ def optimize(
extras={},
)
# Warn if any non-frozen parameter component starts at (or essentially at)
# a box bound. Optimization can stall in that corner on ill-conditioned or
# rank-deficient problems; both the mujoco and scipy backends are affected.
lo, hi = bounds
rng = hi - lo
safe_rng = np.where(rng > 0, rng, 1.0)
at_bound = ((x0 - lo) <= 1e-3 * safe_rng) | ((hi - x0) <= 1e-3 * safe_rng)
at_bound &= rng > 0
if at_bound.any():
logging.warning(
"%d of %d non-frozen parameter components start at (or essentially "
"at) a box bound. Optimization can stall in that corner on "
"ill-conditioned problems; consider calling "
"initial_params.move_off_bounds() before optimize().",
int(at_bound.sum()),
x0.size,
)
def optimized_residual_fn(x):
residuals, _, _ = residual_fn(x, opt_params)
return np.concatenate(residuals)
+22
View File
@@ -139,6 +139,19 @@ class Parameter:
rng = np.random.default_rng()
return rng.uniform(self.min_value.flatten(), self.max_value.flatten())
def move_off_bound(self, fraction: float = 0.05) -> None:
"""Shift values within 0.1% of a bound to ``lo + fraction*(hi-lo)`` (or
symmetric for the upper bound). Interior components are unchanged."""
lo, hi = self.get_bounds()
rng = hi - lo
safe_rng = np.where(rng > 0, rng, 1.0)
v = self.as_vector().copy()
at_lo = (v - lo) <= 1e-3 * safe_rng
at_hi = (hi - v) <= 1e-3 * safe_rng
v = np.where(at_lo, lo + fraction * rng, v)
v = np.where(at_hi, hi - fraction * rng, v)
self.update_from_vector(v)
def __str__(self) -> str:
"""Return a string representation of the parameter."""
if self.size == 1:
@@ -293,6 +306,15 @@ class ParameterDict:
param.update_from_vector(vector[start : start + size])
start += size
def move_off_bounds(self, fraction: float = 0.05) -> Self:
"""Call :meth:`Parameter.move_off_bound` on every non-frozen parameter,
returning ``self`` so calls can be chained before
:func:`optimize`."""
for param in self.parameters.values():
if not param.frozen:
param.move_off_bound(fraction=fraction)
return self
def save_to_disk(self, path: str | pathlib.Path) -> None:
"""Save the parameter dictionary to disk (schema and data).
@@ -146,3 +146,38 @@ def test_frozen_param_excluded():
np.testing.assert_array_equal(params["free"].value, [1.5])
# Frozen param unchanged.
np.testing.assert_array_equal(params["frozen"].value, [5.0])
def test_move_off_bound():
"""At-bound (or essentially at-bound) values shift inward; interior is untouched."""
# At lower bound, at upper bound, essentially-at-lower, interior, vector mixed,
# custom fraction, and degenerate (zero-range) bounds.
cases = [
(0.0, 0.0, 20.0, 0.05, 1.0), # at lower -> 0.05 * 20
(1e-8, 0.0, 20.0, 0.05, 1.0), # essentially at lower
(20.0, 0.0, 20.0, 0.05, 19.0), # at upper -> 20 - 0.05 * 20
(5.0, 0.0, 20.0, 0.05, 5.0), # interior unchanged
(0.0, 0.0, 100.0, 0.1, 10.0), # custom fraction
(3.0, 3.0, 3.0, 0.05, 3.0), # zero-range bound, pinned
]
for nominal, lo, hi, fraction, expected in cases:
p = parameter.Parameter("d", nominal, lo, hi)
p.move_off_bound(fraction=fraction)
np.testing.assert_allclose(p.value, [expected])
# Vector parameter: only at-bound components are shifted.
p = parameter.Parameter(
"v", [0.0, 5.0, 10.0], [0.0, 0.0, 0.0], [10.0, 10.0, 10.0]
)
p.move_off_bound()
np.testing.assert_allclose(p.value, [0.5, 5.0, 9.5])
def test_move_off_bounds_dict_skips_frozen_and_returns_self():
"""ParameterDict shifts free params, leaves frozen alone, returns self."""
pdict = parameter.ParameterDict()
pdict.add(parameter.Parameter("free", 0.0, 0.0, 1.0))
pdict.add(parameter.Parameter("frozen", 0.0, 0.0, 1.0, frozen=True))
assert pdict.move_off_bounds() is pdict
np.testing.assert_allclose(pdict["free"].value, [0.05])
np.testing.assert_allclose(pdict["frozen"].value, [0.0])