Webots sim-to-real fixes, DAgger pipeline, 360° proto variant

Today's session worked across the full Webots delivery stack — found and
fixed a cluster of bugs blocking the BC/RL transfer, then explored
training-side mitigations for the residual perception gap.

Bug fixes:
- Makefile FP_RATE default 2.0 → 0.0: BC demos used fp_rate=0 but RL
  fine-tune defaulted to fp_rate=2, poisoning the BC obs distribution
  and stalling PPO at 0% success across 1.46M+ steps.
- controllers/{shepherd_dog,sheep}/runtime.ini: Webots was launching
  controllers under system python3 (no numpy) and they were crashing
  silently. Pinned to the conda tir env.
- herding/config.py HERDING_WEBOTS preset: pen_latch_depth 0.5 → 2.0,
  max_new_tracks_per_step 3 → 1, static_reject 0.8 → 1.2. Stops phantom
  FPs near the gate from latching as permanently-penned tracks.
- herding/perception/sheep_tracker.py: penned tracks now decay at
  forget_steps × 8 instead of living forever. Adds get_positions
  min_freshness filter for deploy-time use.

Training/eval matches deployment:
- training/bc/collect.py: --dagger-policy flag for DAgger rollouts
  (policy drives, teacher labels) + --use-webots-preset for matched
  140° tracker + DR config.
- controllers/shepherd_dog/shepherd_dog.py: scan-fallback (0, 0.6) when
  BC/RL sees empty sheep_positions — recovers from FOV gaps.

Tooling:
- tools/dagger_round.sh: one-shot DAgger round (collect + concat + bc).
- tools/webots_sweep_gt.sh: full sweep with HERDING_USE_GT=1 for the
  perception-gap diagnosis matrix.
- protos/ShepherdDog360.proto: 360° FOV variant for the FOV-ablation
  comparison. Canonical proto stays at 140° per project spec.

Artifacts: v1 BC/RL policies for all 4 (drive × world) combos trained
in clean gym (success: diff/field 90-100%, diff/round 58%, mec/field
60-100%, mec/round 50-100%). DAgger r1/r2 BCs for diff/field show
12%→38% progression on gym HERDING_WEBOTS proxy but did not close
to actual Webots LiDAR (0/5 throughout). Next: LSTM policy or
learned tracker per the project-state memory.

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

training/eval.py

@@ -18,21 +18,9 @@ from statistics import mean
 
 import numpy as np
 
-# Early CLI pre-parse for --world so geometry is configured before
-# other herding.* modules are imported.
-_pre_argv = [a for a in os.sys.argv[1:]]
-_pre_world = None
-for i, a in enumerate(_pre_argv):
-    if a == "--world" and i + 1 < len(_pre_argv):
-        _pre_world = _pre_argv[i + 1]
-        break
-    if a.startswith("--world="):
-        _pre_world = a.split("=", 1)[1]
-        break
-if _pre_world is not None:
-    from herding.world.geometry import configure as _geo_configure
-    _geo_configure(_pre_world)
-    os.environ["HERDING_WORLD"] = _pre_world
+# Configure field geometry before other herding imports read it at module level.
+from herding.world.geometry import configure_from_args as _configure_from_args
+_configure_from_args()
 
 from herding.world.geometry import MAX_SHEEP, PEN_ENTRY
 from herding.control.sequential import compute_action as sequential_action