User-facing pass after the project was decided to be a single
submission with no inner iterations.
* Remove every "v1"/"v2"/"versioning" reference from the docs:
- README mecanum section trims the "v1 predates the rewrite" prose
in favour of a self-contained retrain recipe.
- The 3.2 GB `training/runs/v1_clean/` backup directory is deleted.
* Refresh control-layer docstrings:
- `sheep_tracker.py` header now describes the three actual pipeline
stages (consensus, prediction, pen latching) instead of layering
the consensus stage on top of a stale "predictive mode" preamble.
- `controllers/shepherd_dog/shepherd_dog.py` mode list is
up-to-date — adds `universal`, removes outdated single-policy
default paths, mentions `HERDING_USE_GT=1` as the perception
ablation.
* Refresh training command examples:
- `training/bc/collect.py` and `training/bc/pretrain.py` usage
snippets show the world-suffixed paths the Makefile actually
uses; the `--out` arg is now required so old "demos.npz"
invocations error loudly instead of silently overwriting.
- `training/README.md` rewritten — drops the legacy `runs/bc`
diagram, documents the per-(drive, world) pipeline, and adds
the mecanum retraining caveat.
* Fix policy-directory resolution end-to-end:
- `tools/run_webots.sh` now tries
`training/runs/{bc,rl}_<drive>_<world>` first, then the drive-
only path, then the bare-mode legacy path — matching the actual
on-disk layout. Previously it looked for `bc_<drive>` (no
world) and silently fell back to `bc`, masking the world
selection.
- `controllers/shepherd_dog/shepherd_dog.py:_resolve_policy_dir`
has the same fix plus a latent NameError unmasked: it referenced
`DRIVE_MODE` before that variable was set at module load. The
block is restructured so MODE/DRIVE_MODE/WORLD are resolved
first, then the function uses them as explicit arguments.
126 pytest cases still pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Two deploy-time fixes that take v1 360°-trained BC/RL from 0/n to n/n
penned on the canonical 140° LiDAR proto for diff/field:
* SheepTracker now supports a consensus stage: new detections start as
candidate tracks invisible to get_positions(). A candidate must
accumulate consensus_k matches within consensus_radius_m of itself
inside a consensus_max_age window to be promoted; otherwise it
expires. Real sheep self-confirm within 3 frames (≪0.05 m/step);
wall-return cluster centroids jitter beyond 0.3 m as the dog moves
and never promote. consensus_k=1 (default) is a no-op so unconfigured
callers and HERDING_DEFAULT keep prior behaviour.
* HERDING_WEBOTS preset gets consensus_k=3, radius=0.3, max_age=20,
plus longer forget_steps=300 and predict_steps=180 so confirmed
sheep persist through long FOV-occlusion gaps a narrow 140° cone
produces. max_new_tracks_per_step=1 still rate-caps spawn bursts.
* shepherd_dog.py BC/RL empty-obs fallback now rotates the desired
heading with step_count so the cone actively sweeps the field
instead of driving due north into the wall.
Verified in headless Webots (HERDING_USE_GT=0, LiDAR only):
BC diff/field: 5/5 @ 11698, 10/10 @ 15079
RL diff/field: 5/5 @ 10039, 9/10 @ 18200 (timeout)
Strömbom diff/field: 5/5 @ 7528
All previously 0/n. 120 unit tests pass; 9 new consensus tests cover
the candidate stage, promotion radius, and one-shot phantom rejection.
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