Portable Python env + 360° LiDAR ablation flag

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>

controllers/sheep/runtime.ini

@@ -1,2 +1,10 @@
+# Webots reads this file before starting the controller. It tells
+# Webots which Python interpreter to launch (default is system
+# `python3`, which usually lacks NumPy).
+#
+# Webots supports environment-variable expansion in this file, so we
+# defer the interpreter path to $HERDING_PYTHON — set that variable
+# once in your shell (or `tools/setup_env.sh`) before launching
+# Webots and the controllers in this project will pick it up.
 [python]
-COMMAND = /home/jalf/miniconda3/envs/tir/bin/python3
+COMMAND = $(HERDING_PYTHON)

controllers/shepherd_dog/runtime.ini

@@ -1,2 +1,10 @@
+# Webots reads this file before starting the controller. It tells
+# Webots which Python interpreter to launch (default is system
+# `python3`, which usually lacks SB3/PyTorch).
+#
+# Webots supports environment-variable expansion in this file, so we
+# defer the interpreter path to $HERDING_PYTHON — set that variable
+# once in your shell (or `tools/setup_env.sh`) before launching
+# Webots and the controllers in this project will pick it up.
 [python]
-COMMAND = /home/jalf/miniconda3/envs/tir/bin/python3
+COMMAND = $(HERDING_PYTHON)

controllers/shepherd_dog/shepherd_dog.py

@@ -97,7 +97,7 @@ from herding.world.geometry import (
     DOG_SOUTH_LIMIT,
     PEN_ENTRY, is_penned,
 )
-from herding.config import HERDING_WEBOTS, RobotConfig
+from herding.config import HERDING_WEBOTS, LIDAR_WEBOTS_360, RobotConfig
 
 # Robot physical constants come from RobotConfig so they stay in sync with
 # the training environment.  The Webots preset uses action_smooth=0.55.
@@ -136,6 +136,18 @@ WORLD = (os.environ.get("HERDING_WORLD")
          or _runtime_cfg.get("HERDING_WORLD")
          or "field").lower()
 
+# LiDAR FOV variant — "140" (default, ShepherdDog.proto) or "360"
+# (ShepherdDog360.proto, FOV ablation). The launcher swaps the proto
+# in the temp world file; the controller picks the matching lidar_cfg
+# below so the perception pipeline interprets ray angles correctly.
+LIDAR_FOV_VARIANT = (os.environ.get("HERDING_LIDAR")
+                     or _runtime_cfg.get("HERDING_LIDAR")
+                     or "140").lower()
+if LIDAR_FOV_VARIANT == "360":
+    _LIDAR_CFG = LIDAR_WEBOTS_360
+else:
+    _LIDAR_CFG = HERDING_WEBOTS.lidar
+
 # Diagnostic: bypass LiDAR tracker and use GT emitter positions directly.
 # Set HERDING_USE_GT=1 to isolate perception sim-to-real gap from policy quality.
 USE_GT_PERCEPTION = bool(int(
@@ -409,7 +421,7 @@ while robot.step(timestep) != -1:
     detections = detections_from_scan(
         ranges, dog_xy[0], dog_xy[1], dog_heading,
         detection_cfg=HERDING_WEBOTS.detection,
-        lidar_cfg=HERDING_WEBOTS.lidar,
+        lidar_cfg=_LIDAR_CFG,
     )
     if USE_GT_PERCEPTION and _gt_sheep:
         # Bypass tracker: feed GT emitter positions directly to policy/teacher.

herding/config.py

@@ -93,6 +93,19 @@ rays), a policy trained here can be deployed on a wider-FOV LiDAR (e.g.
 which can only improve tracker quality.
 """
 
+LIDAR_WEBOTS_360 = LidarConfig(
+    n_rays=360,
+    fov_rad=2.0 * math.pi,
+    max_range=15.0,
+)
+"""Matches ShepherdDog360.proto (360 rays, 360° FOV, 15 m range).
+
+Used by the FOV-ablation Webots launch (HERDING_LIDAR=360). The wider
+range and full surround visibility hand the tracker more detections
+per step, so the trained policy — already trained on 360° gym
+perception — sees an observation distribution closer to training.
+"""
+
 
 # ---------------------------------------------------------------------------
 # Cluster-detection pipeline

tools/calibrate_mecanum.sh

@@ -21,7 +21,7 @@ set -euo pipefail
 N_STEPS="${1:-150}"
 ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )/.." && pwd )"
 LOG="$ROOT/calibrate_mecanum.log"
-export PATH="/home/jalf/miniconda3/envs/tir/bin:$PATH"
+source "$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )/setup_env.sh"
 
 echo "Running mecanum calibration (N_STEPS=$N_STEPS)..."
 echo "Results will be written to: $LOG"

tools/run_webots.sh

@@ -33,6 +33,12 @@
 #   WEBOTS_EXTRA_ARGS="--stdout --stderr" WEBOTS_HEADLESS=1 tools/run_webots.sh 10 rl
 
 set -e
+
+# Make sure HERDING_PYTHON is resolved and on PATH so Webots inherits
+# the right interpreter (controllers/{shepherd_dog,sheep}/runtime.ini
+# both read $HERDING_PYTHON via env-var expansion).
+source "$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )/setup_env.sh"
+
 N=${1:-10}
 MODE=${2:-bc}
 DRIVE=${3:-differential}
@@ -84,12 +90,31 @@ fi
 
 cp "$SRC" "$DST"
 
-# Swap robot proto based on drive mode.
+# LiDAR FOV variant: HERDING_LIDAR=140 (default) or 360 (ablation).
-# Base worlds reference ShepherdDog (diff-drive). For mecanum we swap in
+# 360° is only supported for differential drive; the mecanum proto
-# ShepherdDogMecanum and inject mecanum contact properties.
+# always uses the 140° sensor matching ShepherdDog.proto.
+LIDAR_VARIANT="${HERDING_LIDAR:-140}"
+if [[ "$LIDAR_VARIANT" != "140" && "$LIDAR_VARIANT" != "360" ]]; then
+    echo "HERDING_LIDAR must be 140 or 360, got '$LIDAR_VARIANT'" >&2; exit 1
+fi
+if [[ "$LIDAR_VARIANT" == "360" && "$DRIVE" == "mecanum" ]]; then
+    echo "[run_webots] HERDING_LIDAR=360 not available for mecanum drive — falling back to 140." >&2
+    LIDAR_VARIANT="140"
+fi
+export HERDING_LIDAR="$LIDAR_VARIANT"
+
+# Swap robot proto based on drive mode + LiDAR variant.
+# Base worlds reference ShepherdDog (diff-drive 140°). For mecanum we
+# swap in ShepherdDogMecanum; for the 360° ablation we swap in
+# ShepherdDog360.
 if [[ "$DRIVE" == "mecanum" ]]; then
     sed -i 's|"../protos/ShepherdDog.proto"|"../protos/ShepherdDogMecanum.proto"|' "$DST"
     sed -i 's|^ShepherdDog {|ShepherdDogMecanum {|' "$DST"
+elif [[ "$LIDAR_VARIANT" == "360" ]]; then
+    sed -i 's|"../protos/ShepherdDog.proto"|"../protos/ShepherdDog360.proto"|' "$DST"
+    sed -i 's|^ShepherdDog {|ShepherdDog360 {|' "$DST"
+fi
+if [[ "$DRIVE" == "mecanum" ]]; then
     # Inject mecanum roller contact properties. The proto's rollers are
     # split into two contact materials so that we can keep the friction
     # axes oriented along each roller's free-spin direction — but with
@@ -152,6 +177,7 @@ HERDING_MODE=$MODE
 HERDING_POLICY_DIR=$RESOLVED_POLICY_DIR
 HERDING_DRIVE=$DRIVE
 HERDING_WORLD=$WORLD
+HERDING_LIDAR=$LIDAR_VARIANT
 HERDING_USE_GT=${HERDING_USE_GT:-0}
 EOF
 
@@ -159,6 +185,7 @@ export HERDING_MODE="$MODE"
 export HERDING_POLICY_DIR="$RESOLVED_POLICY_DIR"
 export HERDING_DRIVE="$DRIVE"
 export HERDING_WORLD="$WORLD"
+export HERDING_LIDAR="$LIDAR_VARIANT"
 
 # The controller writes this sentinel when all GT sheep are penned. We
 # poll for it and kill Webots so the run finishes cleanly instead of

tools/setup_env.sh

@@ -0,0 +1,23 @@
+# Source this from your shell before running the launchers:
+#
+#     source tools/setup_env.sh
+#
+# The launchers (`tools/run_webots.sh`, `tools/webots_sweep*.sh`,
+# `tools/calibrate_mecanum.sh`) and the Webots controllers (via
+# `controllers/*/runtime.ini`) all read $HERDING_PYTHON to decide
+# which Python interpreter to use. The default below points at the
+# project author's conda env — edit this file or override the var in
+# your shell to match your own setup.
+
+: "${HERDING_PYTHON:=/home/jalf/miniconda3/envs/tir/bin/python3}"
+export HERDING_PYTHON
+
+# Prepend the Python's bin/ to PATH so subprocesses pick up the same
+# interpreter (used by Webots when it doesn't read runtime.ini, and
+# by any Python tooling launched by the bash scripts).
+export PATH="$(dirname "$HERDING_PYTHON"):$PATH"
+
+if [[ ! -x "$HERDING_PYTHON" ]]; then
+    echo "[setup_env] WARNING: HERDING_PYTHON=$HERDING_PYTHON is not executable." >&2
+    echo "[setup_env] Edit tools/setup_env.sh or 'export HERDING_PYTHON=...' yourself." >&2
+fi

tools/webots_sweep.sh

@@ -10,8 +10,9 @@ ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )/.." && pwd )"
 OUT="${1:-$ROOT/webots_sweep.log}"
 TIMEOUT_S=120  # ~80k steps in fast headless mode — covers slow-converging physics
 
-# Webots uses its own python3; put the conda env first so all deps resolve.
+# Source the project python path. Edit tools/setup_env.sh or override
-export PATH="/home/jalf/miniconda3/envs/tir/bin:$PATH"
+# HERDING_PYTHON in your shell to point at a Python with SB3+PyTorch.
+source "$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )/setup_env.sh"
 
 # Columns: mode  drive  world  n_sheep  success  steps
 printf "%-12s %-14s %-12s %7s  %7s  %s\n" \

tools/webots_sweep_gt.sh

@@ -10,8 +10,9 @@ ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )/.." && pwd )"
 OUT="${1:-$ROOT/webots_sweep.log}"
 TIMEOUT_S=120  # ~80k steps in fast headless mode — covers slow-converging physics
 
-# Webots uses its own python3; put the conda env first so all deps resolve.
+# Source the project python path. Edit tools/setup_env.sh or override
-export PATH="/home/jalf/miniconda3/envs/tir/bin:$PATH"
+# HERDING_PYTHON in your shell to point at a Python with SB3+PyTorch.
+source "$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )/setup_env.sh"
 
 # Columns: mode  drive  world  n_sheep  success  steps
 printf "%-12s %-14s %-12s %7s  %7s  %s\n" \