Johnny Fernandes
dd5ac669e5
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>
101 lines
3.6 KiB
Bash
Executable File
101 lines
3.6 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Headless Webots sweep across modes, drives, worlds, and flock sizes.
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# Runs sequentially; each trial gets a hard 150s wall-clock timeout.
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# Results are written to webots_sweep.log (tab-separated) and printed.
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#
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# Usage: bash tools/webots_sweep.sh [output_log]
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set -euo pipefail
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ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )/.." && pwd )"
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OUT="${1:-$ROOT/webots_sweep.log}"
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TIMEOUT_S=120 # ~80k steps in fast headless mode — covers slow-converging physics
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# Webots uses its own python3; put the conda env first so all deps resolve.
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export PATH="/home/jalf/miniconda3/envs/tir/bin:$PATH"
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# Columns: mode drive world n_sheep success steps
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printf "%-12s %-14s %-12s %7s %7s %s\n" \
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"mode" "drive" "world" "n_sheep" "success" "steps" | tee "$OUT"
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printf '%s\n' "$(printf '%-12s %-14s %-12s %7s %7s %s' \
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'----' '-----' '-----' '-------' '-------' '-----')" | tee -a "$OUT"
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run_trial() {
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local mode="$1" drive="$2" world="$3" n="$4" policy_dir="${5:-}"
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local done_file="$ROOT/training/.run_done"
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rm -f "$done_file"
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local extra_env=()
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extra_env+=(WEBOTS_HEADLESS=1)
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extra_env+=(WEBOTS_EXTRA_ARGS="--stdout --stderr")
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extra_env+=(HERDING_USE_GT=0)
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if [[ -n "$policy_dir" ]]; then
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extra_env+=(HERDING_POLICY_DIR="$ROOT/$policy_dir")
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fi
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local raw
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raw=$(
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timeout --kill-after=15s "$TIMEOUT_S" \
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xvfb-run -a \
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env "${extra_env[@]}" \
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bash "$ROOT/tools/run_webots.sh" "$n" "$mode" "$drive" "$world" \
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2>&1
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) || true
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# Webots-bin and Xvfb can survive the timeout; kill any orphans now.
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pkill -9 -f "webots-bin|Xvfb" 2>/dev/null || true
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sleep 1
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local success="FAIL"
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local steps="timeout"
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if echo "$raw" | grep -q "\[dog\] all .* sheep penned at step"; then
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success="OK"
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steps=$(echo "$raw" | grep "\[dog\] all .* sheep penned at step" \
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| grep -oP 'step \K[0-9]+')
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fi
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printf "%-12s %-14s %-12s %7s %7s %s\n" \
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"$mode" "$drive" "$world" "$n" "$success" "$steps" | tee -a "$OUT"
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}
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# ---------------------------------------------------------------------------
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# Analytic baselines (differential only — that's the story context)
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# strombom / sequential: canonical baselines
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# universal: the actual teacher used to collect BC demos
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# ---------------------------------------------------------------------------
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for mode in strombom sequential universal; do
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for world in field field_round; do
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for n in 5 10; do
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run_trial "$mode" differential "$world" "$n"
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done
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done
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done
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# ---------------------------------------------------------------------------
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# BC — world-specific policies
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# ---------------------------------------------------------------------------
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for drive in differential mecanum; do
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for world in field field_round; do
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for n in 5 10; do
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run_trial bc "$drive" "$world" "$n" \
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"training/runs/bc_${drive}_${world}"
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done
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done
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done
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# ---------------------------------------------------------------------------
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# RL_FAST — MODE=rl with explicit HERDING_POLICY_DIR pointing to rl_fast dirs
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# (run_webots.sh rejects "rl_fast" as a mode; "rl" + policy override is correct)
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# ---------------------------------------------------------------------------
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for drive in differential mecanum; do
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for world in field field_round; do
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for n in 5 10; do
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run_trial rl "$drive" "$world" "$n" \
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"training/runs/rl_fast_${drive}_${world}"
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done
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done
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done
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echo ""
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echo "Sweep complete. Results saved to: $OUT"
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