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16 changes: 9 additions & 7 deletions grl_snam/tools/coef_train.py
Original file line number Diff line number Diff line change
Expand Up @@ -650,9 +650,9 @@ def main(argv=None):
risk_model = sdf_nav.add_risk_feature(sdf_nav.CoefMLP())
if args.learned_lam:
# LEARNED reroute: the net outputs lam_soft per position instead of the fixed
# --lam-soft dial — the deployable reroute lever (needs the C++ coef_mlp/drive
# update before a risk .cvcnav can run on the pure-C++ host). --lam-soft is the
# INIT here and must be > 0 (0 is a valid FIXED-mode value but a dead learned init).
# --lam-soft dial — the deployable reroute lever (drive_step_material reads the 4th
# output as per-agent lam_soft since libcvc #413). --lam-soft is the INIT here and
# must be > 0 (0 is a valid FIXED-mode value but a dead learned init).
if args.lam_soft <= 0.0:
raise SystemExit(
"--learned-lam needs --lam-soft > 0 (it seeds the lam head; 0 would freeze "
Expand Down Expand Up @@ -688,10 +688,12 @@ def main(argv=None):
if getattr(model, "use_risk", False) or getattr(model, "use_lam", False):
extra = f", out_dim={model.out_dim} (lam head)" if getattr(model, "use_lam", False) else ""
print(
f"wrote {args.out} (risk model, in_dim={model.in_dim}{extra}) — NOTE: this is a torch "
"research artifact. The pure-C++ host's coef_feats does not build the risk-lookahead "
"column, and its coef_mlp/drive do not read a lam output, so do NOT deploy this over "
"share/cvc/nav/coef_mlp.cvcnav until the coordinated cvc::nav update lands."
f"wrote {args.out} (grip/risk model, in_dim={model.in_dim}{extra}) — DEPLOYABLE on a "
"libcvc host at or past the risk-lookahead/learned-lam drive update (transfix/libcvc "
"#413): coef_feats builds the risk-lookahead column and drive_step_material reads the "
"lam output. Drive it through the MATERIAL path (drive_step_material / "
"drive_step_material_ext), with a material stack + grip attached — the plain drive_step "
"rejects a risk net. Round-trip + drive verified by libcvc nav_material_deploy_test."
)
else:
print(f"wrote {args.out} reach_rate={reach_rate(model):.2%}")
Expand Down
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