Per-sheep pen-time metrics, seed support, make webots → menu

* `controllers/shepherd_dog/shepherd_dog.py`
  - Tracks the first step at which each sheep crosses the gate; on
    auto-finish (all sheep penned) prints a `[results]` summary
    block: mode/drive/world/lidar/dogs/seed line, total simulated
    time, per-sheep penning order with absolute step + seconds since
    sim start, and the gate spread between the first and last
    penning.
  - Reads `HERDING_SEED` (env / runtime cfg) and seeds the
    controller's RNG when set. Empty = time-based default = old
    non-deterministic behaviour.
* `controllers/sheep/sheep.py` reads `HERDING_SEED` the same way
  (loading `herding_runtime.cfg` itself so it works even when
  Webots strips env vars) and seeds Python's RNG XOR'd with the
  sheep's name hash, so a fixed seed gives a reproducible flock
  trajectory without all sheep starting from identical wander state.
* `tools/run_webots.sh` writes `HERDING_SEED` into the runtime cfg
  (empty when unset so existing scripts keep their stochastic
  behaviour).
* `tools/webots_menu.sh` gains a Seed prompt (random / fixed
  integer); the launch summary box shows the choice next to the
  perception row.
* `Makefile`
  - `make webots`  now fires the interactive picker (replacing the
    old positional invocation).
  - `make webots_quick MODE=… DRIVE=… WORLD=… N=…` is the old
    positional path, kept for batch / scripted use.

Smoke-tested: menu renders Mode → Drive → World → LiDAR → Dogs
→ Sheep → Perception → Seed → Headless prompts and shows the
selected Seed value in the launch summary. 126 pytest cases still
pass.

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

controllers/shepherd_dog/shepherd_dog.py

@@ -351,10 +351,32 @@ if MODE in ("strombom", "sequential"):
 elif MODE == "universal":
     analytic_teacher = ActiveScanTeacher(universal_action)
 
+# Optional deterministic seed for the controller's RNG. The sheep
+# controller seeds itself the same way, so identical HERDING_SEED
+# values give reproducible trials. If unset (empty), Python uses its
+# time-based default and runs are non-deterministic.
+import random as _random
+_seed_raw = (os.environ.get("HERDING_SEED")
+             or _runtime_cfg.get("HERDING_SEED")
+             or "").strip()
+if _seed_raw:
+    try:
+        HERDING_SEED = int(_seed_raw)
+    except ValueError:
+        HERDING_SEED = None
+        print(f"[dog] could not parse HERDING_SEED={_seed_raw!r}; using random")
+    else:
+        _random.seed(HERDING_SEED)
+else:
+    HERDING_SEED = None
+
 # GT positions from sheep emitters — used **only** for the auto-finish
-# sentinel and the GT_penned diagnostic line. Never fed into control.
+# sentinel, the GT_penned diagnostic line, and the per-sheep pen-time
+# metrics printed at end of run. Never fed into control.
 _gt_sheep: dict = {}
+_pen_step: dict = {}   # sheep name -> step at which it first became penned
 _run_done = False
+_t_start = None        # step at which we first see GT positions (sim start)
 
 prev_action = (0.0, 0.0, 0.0) if DRIVE_MODE == "mecanum" else (0.0, 0.0)
 step_count = 0
@@ -536,10 +558,20 @@ while robot.step(timestep) != -1:
     left_ear.setPosition(ear_pos)
     right_ear.setPosition(-ear_pos)
 
-    # Auto-finish: when all GT sheep are penned, write the sentinel.
-    # The launcher polls for it and closes Webots so batch evals don't
-    # hang after the task is done. Bounded by `_gt_sheep` so we don't
-    # fire during the first few steps while the receiver fills.
+    # First step we have GT visibility — record the simulation start
+    # so per-sheep pen times can be reported relative to it.
+    if _gt_sheep and _t_start is None:
+        _t_start = step_count
+
+    # Record the first step at which each sheep is observed penned.
+    for _sname, (_sx, _sy) in _gt_sheep.items():
+        if _sname not in _pen_step and is_penned(_sx, _sy):
+            _pen_step[_sname] = step_count
+
+    # Auto-finish: when all GT sheep are penned, write the sentinel
+    # and print the per-sheep penning summary so the operator sees
+    # the metrics in the terminal. The launcher polls for the
+    # sentinel and closes Webots cleanly.
     if _gt_sheep and not _run_done:
         gt_active = sum(1 for x, y in _gt_sheep.values()
                         if not is_penned(x, y))
@@ -549,6 +581,26 @@ while robot.step(timestep) != -1:
             _run_done = True
             print(f"[dog] all {len(_gt_sheep)} sheep penned at step "
                   f"{step_count} — wrote sentinel, launcher will close Webots")
+            # Only the first dog to detect the finish prints the
+            # summary (in dual-dog mode both run in lock-step but the
+            # sentinel acts as a one-shot lock).
+            _dt = 0.016  # Webots basicTimeStep, seconds
+            print("")
+            print(f"[results] mode={MODE} drive={DRIVE_MODE} world={WORLD} "
+                  f"lidar={LIDAR_FOV_VARIANT} dogs={DOG_NAME}"
+                  + (f" seed={HERDING_SEED}" if HERDING_SEED is not None else ""))
+            print(f"[results] total steps: {step_count}  "
+                  f"({step_count * _dt:.1f} s simulated)")
+            ordered = sorted(_pen_step.items(), key=lambda kv: kv[1])
+            for i, (sn, st) in enumerate(ordered, 1):
+                rel = st - (_t_start or 0)
+                print(f"[results]   #{i}  {sn:8s} penned at step {st:>6d}  "
+                      f"({rel * _dt:6.2f} s)")
+            if len(ordered) >= 2:
+                first = ordered[0][1]
+                last = ordered[-1][1]
+                print(f"[results] gate spread: {last - first} steps "
+                      f"({(last - first) * _dt:.2f} s) between first and last pen")
 
     if step_count % 200 == 0:
         gt_penned = sum(1 for x, y in _gt_sheep.values()