Allow strafe_efficiency=1.0 in mec preset test; minor comment cleanup

After a deep investigation into the n=5 mecanum sim-to-real gap, all
attempted fixes (consensus tightening, wall_reject tightening, static-
phantom drop, deploy-time track merge, in-tracker track merge,
fp_rate-augmented retrain, max_range cap, 140° mecanum retrain) failed
to reliably pen n=5 in Webots without regressing n=10. The phantom
problem at 360° + small flock is genuinely hard and out of scope for
the deadline; documented in docs/status.md.

Result preserved from the previous mecanum work:
* 16/16 differential cells pen N/N.
* 4/8 mecanum cells (all n=10) pen 10/10 via Supervisor kinematic
  injection (commit 27c0f65).
* n=5 mecanum is the known gap.

Small changes that survived the iteration:
* tests/test_config.py — strafe_efficiency=1.0 is now valid (kinematic
  injection means the gym preset and Webots controller share the
  formula, so textbook values produce gym-identical body motion).
* tools/run_webots.sh — refreshed the LiDAR-variant comment.
* training/rl/train.py — comment polish.

tests/test_config.py

@@ -156,8 +156,13 @@ class TestRobotConfig:
 
     def test_mec_webots_preset(self):
         from herding.config import HERDING_MEC_WEBOTS
-        assert 0.0 < HERDING_MEC_WEBOTS.robot.strafe_efficiency < 1.0
+        # Mecanum runs deploy via Supervisor kinematic injection
-        assert HERDING_MEC_WEBOTS.robot.strafe_to_forward_bleed < 0.0
+        # (controllers/shepherd_dog/shepherd_dog.py:drive_mecanum), so
+        # whatever strafe_efficiency/strafe_to_forward_bleed the preset
+        # picks is what Webots will apply. The preset is allowed to be
+        # textbook (1.0, 0.0) or matched (<1.0, ≠0.0).
+        cfg = HERDING_MEC_WEBOTS.robot
+        assert 0.0 < cfg.strafe_efficiency <= 1.0
 
 
 # ---------------------------------------------------------------------------

tools/run_webots.sh

@@ -90,11 +90,9 @@ fi
 
 cp "$SRC" "$DST"
 
-# LiDAR FOV variant. For mecanum the default is 360° because the
+# LiDAR FOV variant. Mecanum defaults to 360° (the trained mecanum
-# physical-roller proto's passive yaw drift makes the 140° front
+# target); diff defaults to 140°. Override with HERDING_LIDAR=140 or
-# cone unreliable (heading errors push detections out of the
+# HERDING_LIDAR=360 for ablations.
-# tracker's world-frame matching gates). Diff defaults to 140°
-# matching the canonical ShepherdDog.proto.
 if [[ -z "${HERDING_LIDAR:-}" ]]; then
     if [[ "$DRIVE" == "mecanum" ]]; then
         LIDAR_VARIANT="360"

training/rl/train.py

@@ -279,7 +279,7 @@ def main() -> None:
         HerdingConfig, HERDING_MEC_WEBOTS_360, DomainRandomConfig, RobotConfig,
     )
     herding_cfg = None
-    # Mecanum always trains under HERDING_MEC_WEBOTS_360 (360° LiDAR +
+    # Mecanum trains under HERDING_MEC_WEBOTS_360 (360° LiDAR +
     # kinematic-matched strafe scaling + small compass-noise DR).
     is_mecanum = (drive_mode == "mecanum")
     if is_mecanum or args.fp_rate > 0.0 or args.action_smooth > 0.0 or args.wheel_slip_std > 0.0: