Gym mecanum kinematics matching to Webots roller-hinge proto

Mecanum proto rewrite in b3cf990 made the wheels truly omnidirectional
in Webots, but with asymmetric slip: forward command produces ~89% of
textbook speed while strafe produces only ~38% plus a consistent
~28% backward bleed-through. v1 BC/RL trained on perfect mecanum
gym kinematics could not herd the new dynamics. To unblock that:

* `mecanum_kinematics_step` gains two parameters that scale the
  realised motion to match a deployed-platform calibration:
    - strafe_efficiency  ∈ (0, 1]  default 1.0
    - strafe_to_forward_bleed     default 0.0
  Forward motion is untouched (textbook X-pattern continues to apply
  to vx_body); only the lateral channel is scaled and bleed is added.
* `RobotConfig` exposes both as drive-config fields with the same
  pass-through defaults so existing diff-drive code and existing
  mecanum training pipelines see no behaviour change.
* `HERDING_MEC_WEBOTS` preset bakes in the values measured against the
  current Webots mecanum proto (strafe_efficiency=0.4,
  strafe_to_forward_bleed=-0.28). Training mecanum BC/RL with this
  preset produces policies that compensate for the imperfect
  physical mecanum at deploy.
* `HerdingEnv` plumbs `RobotConfig.strafe_*` through to
  `mecanum_kinematics_step` so the preset takes effect.
* tools/gen_mecanum_wheels.py is added so the proto's 32 roller
  hinges can be regenerated by editing a single set of constants
  rather than hand-editing 1500+ lines of VRML.

Tests:
* 4 new mecanum_kinematics_step tests (default pass-through, strafe
  scaling, backward bleed, forward unaffected by strafe params).
* 3 new RobotConfig tests (defaults, validation, preset shape).
* Sanity check: gym strafe with HERDING_MEC_WEBOTS over 100 steps
  reproduces the Webots calibration to 2 decimal places.

126 unit tests pass (was 120).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

training/herding_env.py

@@ -296,6 +296,12 @@ class HerdingEnv(gym.Env):
                 k_turn=4.0,
                 wheel_base=DOG_WHEEL_BASE,
             )
+            robot_cfg = (self._herding_cfg.robot
+                         if self._herding_cfg is not None else None)
+            strafe_efficiency = (robot_cfg.strafe_efficiency
+                                 if robot_cfg is not None else 1.0)
+            strafe_bleed = (robot_cfg.strafe_to_forward_bleed
+                            if robot_cfg is not None else 0.0)
             self.dog_x, self.dog_y, self.dog_heading = mecanum_kinematics_step(
                 self.dog_x, self.dog_y, self.dog_heading,
                 w_fl, w_fr, w_rl, w_rr,
@@ -304,6 +310,8 @@ class HerdingEnv(gym.Env):
                 WEBOTS_DT,
                 slip_std=slip_std,
                 rng=self._np_rng_lidar,
+                strafe_efficiency=strafe_efficiency,
+                strafe_to_forward_bleed=strafe_bleed,
             )
         else:
             wL, wR = velocity_to_wheels(