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>
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