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/webots_sweep.sh

@@ -0,0 +1,100 @@
+#!/usr/bin/env bash
+# Headless Webots sweep across modes, drives, worlds, and flock sizes.
+# Runs sequentially; each trial gets a hard 150s wall-clock timeout.
+# Results are written to webots_sweep.log (tab-separated) and printed.
+#
+# Usage: bash tools/webots_sweep.sh [output_log]
+
+set -euo pipefail
+ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )/.." && pwd )"
+OUT="${1:-$ROOT/webots_sweep.log}"
+TIMEOUT_S=120  # ~80k steps in fast headless mode — covers slow-converging physics
+
+# Webots uses its own python3; put the conda env first so all deps resolve.
+export PATH="/home/jalf/miniconda3/envs/tir/bin:$PATH"
+
+# Columns: mode  drive  world  n_sheep  success  steps
+printf "%-12s %-14s %-12s %7s  %7s  %s\n" \
+    "mode" "drive" "world" "n_sheep" "success" "steps" | tee "$OUT"
+printf '%s\n' "$(printf '%-12s %-14s %-12s %7s  %7s  %s' \
+    '----' '-----' '-----' '-------' '-------' '-----')" | tee -a "$OUT"
+
+run_trial() {
+    local mode="$1" drive="$2" world="$3" n="$4" policy_dir="${5:-}"
+
+    local done_file="$ROOT/training/.run_done"
+    rm -f "$done_file"
+
+    local extra_env=()
+    extra_env+=(WEBOTS_HEADLESS=1)
+    extra_env+=(WEBOTS_EXTRA_ARGS="--stdout --stderr")
+    extra_env+=(HERDING_USE_GT=0)
+    if [[ -n "$policy_dir" ]]; then
+        extra_env+=(HERDING_POLICY_DIR="$ROOT/$policy_dir")
+    fi
+
+    local raw
+    raw=$(
+        timeout --kill-after=15s "$TIMEOUT_S" \
+        xvfb-run -a \
+        env "${extra_env[@]}" \
+        bash "$ROOT/tools/run_webots.sh" "$n" "$mode" "$drive" "$world" \
+        2>&1
+    ) || true
+    # Webots-bin and Xvfb can survive the timeout; kill any orphans now.
+    pkill -9 -f "webots-bin|Xvfb" 2>/dev/null || true
+    sleep 1
+
+    local success="FAIL"
+    local steps="timeout"
+
+    if echo "$raw" | grep -q "\[dog\] all .* sheep penned at step"; then
+        success="OK"
+        steps=$(echo "$raw" | grep "\[dog\] all .* sheep penned at step" \
+                | grep -oP 'step \K[0-9]+')
+    fi
+
+    printf "%-12s %-14s %-12s %7s  %7s  %s\n" \
+        "$mode" "$drive" "$world" "$n" "$success" "$steps" | tee -a "$OUT"
+}
+
+# ---------------------------------------------------------------------------
+# Analytic baselines (differential only — that's the story context)
+# strombom / sequential: canonical baselines
+# universal: the actual teacher used to collect BC demos
+# ---------------------------------------------------------------------------
+for mode in strombom sequential universal; do
+    for world in field field_round; do
+        for n in 5 10; do
+            run_trial "$mode" differential "$world" "$n"
+        done
+    done
+done
+
+# ---------------------------------------------------------------------------
+# BC — world-specific policies
+# ---------------------------------------------------------------------------
+for drive in differential mecanum; do
+    for world in field field_round; do
+        for n in 5 10; do
+            run_trial bc "$drive" "$world" "$n" \
+                "training/runs/bc_${drive}_${world}"
+        done
+    done
+done
+
+# ---------------------------------------------------------------------------
+# RL_FAST — MODE=rl with explicit HERDING_POLICY_DIR pointing to rl_fast dirs
+# (run_webots.sh rejects "rl_fast" as a mode; "rl" + policy override is correct)
+# ---------------------------------------------------------------------------
+for drive in differential mecanum; do
+    for world in field field_round; do
+        for n in 5 10; do
+            run_trial rl "$drive" "$world" "$n" \
+                "training/runs/rl_fast_${drive}_${world}"
+        done
+    done
+done
+
+echo ""
+echo "Sweep complete. Results saved to: $OUT"