Johnny Fernandes
d00da52c3c
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>
102 lines
3.6 KiB
Bash
Executable File
102 lines
3.6 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Headless Webots sweep across modes, drives, worlds, and flock sizes.
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# Runs sequentially; each trial gets a hard 150s wall-clock timeout.
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# Results are written to webots_sweep.log (tab-separated) and printed.
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#
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# Usage: bash tools/webots_sweep.sh [output_log]
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set -euo pipefail
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ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )/.." && pwd )"
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OUT="${1:-$ROOT/webots_sweep.log}"
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TIMEOUT_S=120 # ~80k steps in fast headless mode — covers slow-converging physics
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# Source the project python path. Edit tools/setup_env.sh or override
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# HERDING_PYTHON in your shell to point at a Python with SB3+PyTorch.
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source "$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )/setup_env.sh"
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# Columns: mode drive world n_sheep success steps
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printf "%-12s %-14s %-12s %7s %7s %s\n" \
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"mode" "drive" "world" "n_sheep" "success" "steps" | tee "$OUT"
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printf '%s\n' "$(printf '%-12s %-14s %-12s %7s %7s %s' \
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'----' '-----' '-----' '-------' '-------' '-----')" | tee -a "$OUT"
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run_trial() {
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local mode="$1" drive="$2" world="$3" n="$4" policy_dir="${5:-}"
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local done_file="$ROOT/training/.run_done"
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rm -f "$done_file"
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local extra_env=()
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extra_env+=(WEBOTS_HEADLESS=1)
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extra_env+=(WEBOTS_EXTRA_ARGS="--stdout --stderr")
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extra_env+=(HERDING_USE_GT=1)
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if [[ -n "$policy_dir" ]]; then
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extra_env+=(HERDING_POLICY_DIR="$ROOT/$policy_dir")
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fi
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local raw
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raw=$(
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timeout --kill-after=15s "$TIMEOUT_S" \
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xvfb-run -a \
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env "${extra_env[@]}" \
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bash "$ROOT/tools/run_webots.sh" "$n" "$mode" "$drive" "$world" \
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2>&1
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) || true
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# Webots-bin and Xvfb can survive the timeout; kill any orphans now.
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pkill -9 -f "webots-bin|Xvfb" 2>/dev/null || true
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sleep 1
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local success="FAIL"
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local steps="timeout"
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if echo "$raw" | grep -q "\[dog\] all .* sheep penned at step"; then
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success="OK"
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steps=$(echo "$raw" | grep "\[dog\] all .* sheep penned at step" \
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| grep -oP 'step \K[0-9]+')
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fi
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printf "%-12s %-14s %-12s %7s %7s %s\n" \
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"$mode" "$drive" "$world" "$n" "$success" "$steps" | tee -a "$OUT"
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}
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# ---------------------------------------------------------------------------
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# Analytic baselines (differential only — that's the story context)
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# strombom / sequential: canonical baselines
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# universal: the actual teacher used to collect BC demos
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# ---------------------------------------------------------------------------
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for mode in strombom sequential universal; do
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for world in field field_round; do
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for n in 5 10; do
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run_trial "$mode" differential "$world" "$n"
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done
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done
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done
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# ---------------------------------------------------------------------------
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# BC — world-specific policies
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# ---------------------------------------------------------------------------
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for drive in differential mecanum; do
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for world in field field_round; do
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for n in 5 10; do
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run_trial bc "$drive" "$world" "$n" \
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"training/runs/bc_${drive}_${world}"
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done
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done
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done
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# ---------------------------------------------------------------------------
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# RL_FAST — MODE=rl with explicit HERDING_POLICY_DIR pointing to rl_fast dirs
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# (run_webots.sh rejects "rl_fast" as a mode; "rl" + policy override is correct)
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# ---------------------------------------------------------------------------
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for drive in differential mecanum; do
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for world in field field_round; do
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for n in 5 10; do
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run_trial rl "$drive" "$world" "$n" \
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"training/runs/rl_fast_${drive}_${world}"
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done
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done
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done
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echo ""
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echo "Sweep complete. Results saved to: $OUT"
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