Two small features.
(1) Portable interpreter
* `tools/setup_env.sh` exports HERDING_PYTHON (default points to the
project's conda env; override in your shell to retarget).
* Both `controllers/*/runtime.ini` files now use Webots' env-var
expansion: `COMMAND = $(HERDING_PYTHON)` so the Webots-launched
controllers pick up the same interpreter as the bash scripts.
* `tools/run_webots.sh`, `tools/webots_sweep{,_gt}.sh` and
`tools/calibrate_mecanum.sh` all source `setup_env.sh` at the top
instead of hard-coding `/home/jalf/miniconda3/envs/tir/bin`.
The hard-coded conda path is now exactly one line in `setup_env.sh`'s
fallback default — a single place to edit on a new machine, or
override-once via `export HERDING_PYTHON=...`.
(2) 360° LiDAR FOV ablation
* New `LIDAR_WEBOTS_360` preset matches the existing
`protos/ShepherdDog360.proto` (360 rays / 2π FOV / 15 m range).
* `tools/run_webots.sh` reads `HERDING_LIDAR=140|360` and swaps the
diff-drive proto accordingly (mecanum keeps 140° — the
ShepherdDogMecanum proto has its own LiDAR section). The variant
is written into `herding_runtime.cfg` so the controller can read
it even when Webots strips env vars.
* `controllers/shepherd_dog/shepherd_dog.py` picks the matching
`lidar_cfg` (`HERDING_WEBOTS.lidar` for 140°, `LIDAR_WEBOTS_360`
otherwise) and feeds it to `detections_from_scan` so the
perception pipeline interprets ray angles + max range correctly.
Smoke test: `HERDING_LIDAR=360 tools/run_webots.sh 5 strombom
differential field` launches with `ShepherdDog360.proto`, the
controller logs the new mode/drive/world line, and the dog is
penning sheep through 360° perception (4/5 at step 19200 before I
killed the test). No retraining required because the gym already
trains under `LIDAR_FULL` (360° preset).
126 pytest cases still pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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>
Mecanum proto rewrite in b3cf990 made the wheels truly omnidirectional
in Webots, but with asymmetric slip: forward command produces ~89% of
textbook speed while strafe produces only ~38% plus a consistent
~28% backward bleed-through. v1 BC/RL trained on perfect mecanum
gym kinematics could not herd the new dynamics. To unblock that:
* `mecanum_kinematics_step` gains two parameters that scale the
realised motion to match a deployed-platform calibration:
- strafe_efficiency ∈ (0, 1] default 1.0
- strafe_to_forward_bleed default 0.0
Forward motion is untouched (textbook X-pattern continues to apply
to vx_body); only the lateral channel is scaled and bleed is added.
* `RobotConfig` exposes both as drive-config fields with the same
pass-through defaults so existing diff-drive code and existing
mecanum training pipelines see no behaviour change.
* `HERDING_MEC_WEBOTS` preset bakes in the values measured against the
current Webots mecanum proto (strafe_efficiency=0.4,
strafe_to_forward_bleed=-0.28). Training mecanum BC/RL with this
preset produces policies that compensate for the imperfect
physical mecanum at deploy.
* `HerdingEnv` plumbs `RobotConfig.strafe_*` through to
`mecanum_kinematics_step` so the preset takes effect.
* tools/gen_mecanum_wheels.py is added so the proto's 32 roller
hinges can be regenerated by editing a single set of constants
rather than hand-editing 1500+ lines of VRML.
Tests:
* 4 new mecanum_kinematics_step tests (default pass-through, strafe
scaling, backward bleed, forward unaffected by strafe params).
* 3 new RobotConfig tests (defaults, validation, preset shape).
* Sanity check: gym strafe with HERDING_MEC_WEBOTS over 100 steps
reproduces the Webots calibration to 2 decimal places.
126 unit tests pass (was 120).
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>
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