Naming pass: rename functions whose third+ segment is redundant or
implementation-detail, sticking to the codebase's preferred
``noun_verb`` / ``verb_noun`` two-concept idiom. Renames are atomic
across definitions, callers, and tests.
is_penned_position → is_penned
modulate_speed_near_sheep → modulate_speed
mecanum_kinematics_step → mecanum_step
policy_forward_mean → forward_mean
Two-concept patterns like ``velocity_to_wheels`` / ``detections_from_scan``
/ ``make_strombom_predictor`` are left alone — they're idiomatic
converters / factories that read as a single concept, and the longer
form aids grep-ability.
Docstring polish:
* ``herding/config.py`` header drops the "previously lived as a
module-level literal" historical framing — we ship as a single
thing, so the refactor anecdote no longer earns its keep. The
usage examples now mention both ``HERDING_WEBOTS`` and
``HERDING_MEC_WEBOTS`` presets.
126 pytest cases still pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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>
Two changes that together raise diff/round gym success ~52%→88% (BC)
and ~68%→88% (RL) without retraining; diff/field stays at 100%.
* TrackerConfig.consensus_k default 1 → 3 (radius 0.5 m, max_age 15
frames). The same candidate-promotion mechanism that closed the
Webots LiDAR gap also filters gym tracker phantoms — they show up
on the round field where sheep run further between detection
cycles than GATE_M, so each new position spawns a fresh track
while the stale one persists in memory. SheepTracker() called with
no tracker_cfg keeps the legacy pass-through behaviour for
backwards compatibility.
* Strömbom + universal teachers now detect when the natural
"behind the flock" drive target leaves the curved boundary and
fall back to pushing the flock radially inward toward the centre.
Breaks the wall-circling pattern that previously trapped both the
analytical baselines and the trained policies.
A/B numbers (n_sheep ∈ {1,2,3,5,10}, 5 seeds each, max_steps=15000):
diff/field bc: baseline 100% consensus 100%
diff/field rl: baseline 100% consensus 100%
diff/round bc: baseline 52% consensus 88%
diff/round rl: baseline 68% consensus 88%
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