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>

tools/dagger_round.sh

@@ -0,0 +1,67 @@
+#!/usr/bin/env bash
+# Run one DAgger round on a (drive, world) combo.
+#
+# Usage: tools/dagger_round.sh <drive> <world> [seeds_per_n] [round_idx]
+#
+# Collects DAgger demos using the current BC policy as the actor and the
+# universal teacher as the labeller, in the HERDING_WEBOTS preset env
+# (140° FOV, tight tracker — matches deployment). Concatenates with the
+# original BC demos, re-trains BC, and saves to runs/bc_dagger_<combo>/.
+
+set -euo pipefail
+ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )/.." && pwd )"
+cd "$ROOT"
+
+DRIVE="${1:-differential}"
+WORLD="${2:-field}"
+SEEDS="${3:-15}"
+ROUND="${4:-1}"
+
+TAG="${DRIVE}_${WORLD}"
+ORIG_DEMOS="training/bc/demos_${TAG}.npz"
+DAGGER_DEMOS="training/bc/dagger${ROUND}_${TAG}.npz"
+COMBINED_DEMOS="training/bc/combined${ROUND}_${TAG}.npz"
+BC_DIR="training/runs/bc_${TAG}"
+OUT_DIR="training/runs/bc_dagger${ROUND}_${TAG}"
+
+case "$WORLD" in
+    field_round)
+        EPOCHS=150
+        ;;
+    *)
+        EPOCHS=60
+        ;;
+esac
+
+echo "=== DAgger round ${ROUND}: ${DRIVE}/${WORLD} ==="
+echo "    Actor policy: ${BC_DIR}/policy.zip"
+echo "    Output:       ${OUT_DIR}/policy.zip"
+
+# 1. Collect DAgger demos: BC drives, teacher labels (privileged + HERDING_WEBOTS).
+python -m training.bc.collect \
+    --teacher universal --out "$DAGGER_DEMOS" \
+    --seeds-per-n "$SEEDS" --subsample 3 \
+    --frame-stack 4 --drive-mode "$DRIVE" --world "$WORLD" \
+    --max-steps 30000 \
+    --privileged --use-webots-preset \
+    --fp-rate 0.0 --action-smooth 0.55 --wheel-slip-std 0.05 \
+    --dagger-policy "$BC_DIR"
+
+# 2. Concatenate original demos + dagger demos.
+python - <<PY
+import numpy as np
+orig = np.load("${ORIG_DEMOS}")
+dag  = np.load("${DAGGER_DEMOS}")
+obs = np.concatenate([orig["obs"], dag["obs"]], axis=0)
+act = np.concatenate([orig["actions"], dag["actions"]], axis=0)
+np.savez("${COMBINED_DEMOS}", obs=obs, actions=act,
+         meta=np.concatenate([orig["meta"], dag["meta"]], axis=0))
+print(f"[combine] orig={orig['obs'].shape[0]} + dagger={dag['obs'].shape[0]} = {obs.shape[0]}")
+PY
+
+# 3. Re-train BC on combined demos.
+python -m training.bc.pretrain \
+    --demos "$COMBINED_DEMOS" --out "$OUT_DIR" \
+    --epochs "$EPOCHS" --net-arch 512,512
+
+echo "=== DAgger round ${ROUND} done: ${OUT_DIR}/policy.zip ==="