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>
58 lines
2.0 KiB
Bash
Executable File
58 lines
2.0 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Measure the actual velocity response of the Webots mecanum robot and
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# compare against the gym's first-order kinematics prediction.
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#
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# Uses HERDING_MODE=calibrate in the shepherd_dog controller, which applies
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# a known fixed action for N steps, records GPS displacement, and computes
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# the relative error vs gym prediction.
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#
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# Usage:
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# bash tools/calibrate_mecanum.sh [N_STEPS]
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# N_STEPS : steps to hold each action (default 150, ≈ 2.4 s real-time)
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#
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# Output:
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# calibrate_mecanum.log — per-axis results printed and written here
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#
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# Target: < 10% relative error on each axis.
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# If errors are high, tune coulombFriction / forceDependentSlip in
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# tools/run_webots.sh (mecanum contactProperties block).
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set -euo pipefail
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N_STEPS="${1:-150}"
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ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )/.." && pwd )"
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LOG="$ROOT/calibrate_mecanum.log"
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export PATH="/home/jalf/miniconda3/envs/tir/bin:$PATH"
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echo "Running mecanum calibration (N_STEPS=$N_STEPS)..."
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echo "Results will be written to: $LOG"
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truncate -s 0 "$LOG"
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run_calib() {
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local vx="$1" vy="$2" om="$3"
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echo " Testing vx=$vx vy=$vy om=$om ..."
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rm -f "$ROOT/training/.run_done"
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timeout --kill-after=15s 60 \
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xvfb-run -a \
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env WEBOTS_HEADLESS=1 WEBOTS_EXTRA_ARGS="--stdout --stderr" \
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HERDING_MODE=calibrate HERDING_DRIVE=mecanum HERDING_WORLD=field \
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CALIB_VX="$vx" CALIB_VY="$vy" CALIB_OM="$om" \
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CALIB_N_STEPS="$N_STEPS" \
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bash "$ROOT/tools/run_webots.sh" 0 calibrate mecanum field \
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2>&1 | grep -E "cmd=|gym|webots|error" || true
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pkill -9 -f "webots-bin|Xvfb" 2>/dev/null || true
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sleep 1
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}
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# Three test vectors: pure-x, pure-y, diagonal
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run_calib 0.5 0.0 0.0
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run_calib 0.0 0.5 0.0
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run_calib 0.35 0.35 0.0
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echo ""
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echo "=== Calibration results ==="
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cat "$LOG" 2>/dev/null || echo "(no results written — check controller output above)"
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echo ""
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echo "Target: <10% error on each axis."
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echo "If errors are high, tune coulombFriction / forceDependentSlip in"
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echo "tools/run_webots.sh (mecanum contactProperties block)."
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