Enable consensus tracker by default + round-world Strömbom fix

Two changes that together raise diff/round gym success ~52%→88% (BC)
and ~68%→88% (RL) without retraining; diff/field stays at 100%.

* TrackerConfig.consensus_k default 1 → 3 (radius 0.5 m, max_age 15
  frames). The same candidate-promotion mechanism that closed the
  Webots LiDAR gap also filters gym tracker phantoms — they show up
  on the round field where sheep run further between detection
  cycles than GATE_M, so each new position spawns a fresh track
  while the stale one persists in memory. SheepTracker() called with
  no tracker_cfg keeps the legacy pass-through behaviour for
  backwards compatibility.
* Strömbom + universal teachers now detect when the natural
  "behind the flock" drive target leaves the curved boundary and
  fall back to pushing the flock radially inward toward the centre.
  Breaks the wall-circling pattern that previously trapped both the
  analytical baselines and the trained policies.

A/B numbers (n_sheep ∈ {1,2,3,5,10}, 5 seeds each, max_steps=15000):

  diff/field  bc:  baseline 100%  consensus 100%
  diff/field  rl:  baseline 100%  consensus 100%
  diff/round  bc:  baseline  52%  consensus  88%
  diff/round  rl:  baseline  68%  consensus  88%

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

herding/control/strombom.py

@@ -10,7 +10,10 @@ Reference: Strömbom et al. 2014, "Solving the shepherding problem."
 
 import math
 
-from herding.world.geometry import PEN_ENTRY, GATE_Y, in_pen
+from herding.world.geometry import (
+    FIELD_ROUND_R, FIELD_SHAPE,
+    PEN_ENTRY, GATE_Y, in_pen,
+)
 
 F_FACTOR = 4.0       # collect/drive threshold scaled by √n
 DELTA_COLLECT = 1.5  # drive-position offset behind the furthest sheep
@@ -54,6 +57,23 @@ def compute_action(dog_xy, sheep_positions, pen_target=PEN_ENTRY):
         tx, ty = com_x + DELTA_DRIVE * ux, com_y + DELTA_DRIVE * uy
         mode = "drive"
 
+    # Round-field wall fallback: if the drive target lies outside the
+    # curved boundary, push the flock radially inward first so it
+    # leaves the wall — otherwise the dog ends up tangent to the wall
+    # and the flock circles indefinitely.
+    if FIELD_SHAPE == "field_round" and mode == "drive":
+        if math.hypot(tx, ty) > FIELD_ROUND_R - 1.0:
+            r_com = math.hypot(com_x, com_y)
+            if r_com > 1e-3:
+                ux2, uy2 = com_x / r_com, com_y / r_com
+                tx = com_x + DELTA_DRIVE * ux2
+                ty = com_y + DELTA_DRIVE * uy2
+                r_t = math.hypot(tx, ty)
+                if r_t > FIELD_ROUND_R - 1.0:
+                    scale = (FIELD_ROUND_R - 1.0) / r_t
+                    tx *= scale
+                    ty *= scale
+
     ax, ay = _unit(tx - dog_xy[0], ty - dog_xy[1])
     return ax, ay, mode
 

herding/control/universal.py

@@ -29,6 +29,7 @@ For differential drive ``omega`` is always 0.0 and can be ignored.
 import math
 
 from herding.world.geometry import (
+    FIELD_ROUND_R, FIELD_SHAPE,
     PEN_ENTRY, GATE_X, GATE_Y, in_pen,
 )
 
@@ -171,6 +172,28 @@ def compute_action(dog_xy, dog_heading, sheep_positions,
         mode = "drive"
         face_target = pen_target
 
+    # On the round field the natural "behind the flock" point can fall
+    # outside the curved wall when the flock CoM is itself close to the
+    # wall. The dog tries to reach an unreachable target, ends up
+    # tangent to the wall, and the flock circles indefinitely.
+    # Fix: when the natural target leaves the field, fall back to
+    # pushing the flock radially inward toward the centre — break the
+    # wall-circle pattern, then resume normal pen-direction drive once
+    # the flock is back in the interior.
+    if FIELD_SHAPE == "field_round" and mode == "drive":
+        if math.hypot(tx, ty) > FIELD_ROUND_R - 1.0:
+            r_com = math.hypot(com_x, com_y)
+            if r_com > 1e-3:
+                ux2, uy2 = com_x / r_com, com_y / r_com
+                tx = com_x + DELTA_DRIVE * ux2
+                ty = com_y + DELTA_DRIVE * uy2
+                # Clamp to inside-field radius so the dog target is reachable.
+                r_t = math.hypot(tx, ty)
+                if r_t > FIELD_ROUND_R - 1.0:
+                    scale = (FIELD_ROUND_R - 1.0) / r_t
+                    tx *= scale
+                    ty *= scale
+
     ax, ay = _unit(tx - dog_xy[0], ty - dog_xy[1])
 
     # ---- Omega (mecanum only) ----