Repo hygiene pass after a long working session.
Files removed:
* stage1_train.log — runtime training log (~125 KB), shouldn't have
been tracked.
* training/bc/demos.npz — orphan default-name demos file from before
the world+drive-suffixed naming convention took over; no script
references it.
* training/runs/bc_dagger{1,2}_differential_field/policy.zip — failed
DAgger experiment artifacts. Per `memory/dagger_results.md` the
whole DAgger experiment hit 0/5 on Webots transfer; these checkpoints
have no consumers.
Untracked-but-deleted (no git change) — also cleaned from disk:
* Root-level runtime logs (43 *.log files, all unused — gitignored now).
* training/bc/{combined,dagger}*.npz (5 huge demo blobs, 2.6 GB
reclaimed; not committed).
* training/bc/v1/ (2.6 GB backup of pre-DAgger demos; reclaimed).
* training/runs/at_20260426_*/ (orphan timestamped runs; reclaimed).
* All __pycache__/.
Dead code removed:
* `herding/control/strombom.py::compute_action_debug` — no callers
anywhere in the repo.
* `herding/control/sequential.py::compute_action_debug` — same.
* `herding/control/universal.py::compute_action_diff` — same.
.gitignore extended to cover:
* All *.log files (training/eval/webots logs are runtime artifacts).
* training/bc/*.npz (re-collectable on demand by `make bc_demos`).
* training/bc/v1/.
* .pytest_cache, *.pyc, .claude/.
README refreshed:
* Mecanum + round-world coverage in the headline.
* Quick-start updated for DRIVE/WORLD-suffixed Makefile targets,
GT-bypass example, and the mecanum-retrain caveat.
* Layout reflects the actual current tree (config.py, both protos,
both worlds, all tools).
* Results table replaced with the Webots end-to-end numbers from
the 2026-05-16 sweep (8/8 diff combos + LiDAR/GT comparison).
Verification: 126 pytest cases still pass (was 126 going in — no
test-coverage regression from the dead-code removal).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* training/bc/collect.py: --use-webots-preset now picks the
drive-matched variant. Mecanum drives get HERDING_MEC_WEBOTS
(with the Webots-calibrated strafe efficiency and bleed) so the
collected demos reflect the imperfect physical mecanum the
deployed policy will see. Differential drives still use
HERDING_WEBOTS (no behaviour change there).
* training/rl/train.py: mecanum fine-tune now *unconditionally*
applies the HERDING_MEC_WEBOTS robot config to the PPO env (the
policy must update against the same imperfect kinematics it
deploys on). Diff fine-tune unchanged.
To retrain a mecanum policy end-to-end against the new proto:
python -m training.bc.collect --drive-mode mecanum --world field \
--use-webots-preset \
--out training/bc/demos_mecanum_field_v2.npz
python -m training.bc.pretrain --demos training/bc/demos_mecanum_field_v2.npz \
--out training/runs/bc_mecanum_field_v2 ...
python -m training.rl.train --bc training/runs/bc_mecanum_field_v2 \
--out training/runs/rl_mecanum_field_v2 \
--drive-mode mecanum --world field --use-webots-preset
The same flow for field_round / mecanum/round.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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>
Johnny Fernandes