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Project-wide cleanup: gitignore, dead code, stale artifacts, README

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Repo hygiene pass after a long working session.

Files removed:
* stage1_train.log — runtime training log (~125 KB), shouldn't have
  been tracked.
* training/bc/demos.npz — orphan default-name demos file from before
  the world+drive-suffixed naming convention took over; no script
  references it.
* training/runs/bc_dagger{1,2}_differential_field/policy.zip — failed
  DAgger experiment artifacts. Per `memory/dagger_results.md` the
  whole DAgger experiment hit 0/5 on Webots transfer; these checkpoints
  have no consumers.

Untracked-but-deleted (no git change) — also cleaned from disk:
* Root-level runtime logs (43 *.log files, all unused — gitignored now).
* training/bc/{combined,dagger}*.npz (5 huge demo blobs, 2.6 GB
  reclaimed; not committed).
* training/bc/v1/ (2.6 GB backup of pre-DAgger demos; reclaimed).
* training/runs/at_20260426_*/ (orphan timestamped runs; reclaimed).
* All __pycache__/.

Dead code removed:
* `herding/control/strombom.py::compute_action_debug` — no callers
  anywhere in the repo.
* `herding/control/sequential.py::compute_action_debug` — same.
* `herding/control/universal.py::compute_action_diff` — same.

.gitignore extended to cover:
* All *.log files (training/eval/webots logs are runtime artifacts).
* training/bc/*.npz (re-collectable on demand by `make bc_demos`).
* training/bc/v1/.
* .pytest_cache, *.pyc, .claude/.

README refreshed:
* Mecanum + round-world coverage in the headline.
* Quick-start updated for DRIVE/WORLD-suffixed Makefile targets,
  GT-bypass example, and the mecanum-retrain caveat.
* Layout reflects the actual current tree (config.py, both protos,
  both worlds, all tools).
* Results table replaced with the Webots end-to-end numbers from
  the 2026-05-16 sweep (8/8 diff combos + LiDAR/GT comparison).

Verification: 126 pytest cases still pass (was 126 going in — no
test-coverage regression from the dead-code removal).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
Johnny Fernandes
2026-05-17 01:38:19 +00:00
parent 3b4c99a6c4
commit a584a034e9
9 changed files with 127 additions and 182 deletions
Download Patch File Download Diff File Expand all files Collapse all files
herding/control/sequential.py
-45
View File
@@ -80,48 +80,3 @@ def compute_action(dog_xy, sheep_positions, pen_target=PEN_ENTRY):
ax, ay = _unit(tx - dog_xy[0], ty - dog_xy[1])
return ax, ay, mode
def compute_action_debug(dog_xy, sheep_positions, pen_target=PEN_ENTRY):
"""``compute_action`` plus a debug dict."""
active = [(x, y) for (x, y) in sheep_positions.values() if _is_active(x, y)]
if not active:
return 0.0, 0.0, "idle", {
"n_active": 0, "phase": "idle", "radius": 0.0, "threshold": 0.0,
"com_x": 0.0, "com_y": 0.0,
"target_x": dog_xy[0], "target_y": dog_xy[1],
}
n = len(active)
com_x = sum(p[0] for p in active) / n
com_y = sum(p[1] for p in active) / n
dists = [math.hypot(p[0] - com_x, p[1] - com_y) for p in active]
radius = max(dists)
threshold = F_FACTOR * math.sqrt(n)
if n <= STRAGGLER_THRESHOLD:
sx, sy = min(active,
key=lambda p: math.hypot(p[0] - pen_target[0],
p[1] - pen_target[1]))
ux, uy = _unit(sx - pen_target[0], sy - pen_target[1])
tx, ty = sx + DELTA_TARGET * ux, sy + DELTA_TARGET * uy
mode = "targeted"
elif radius > threshold:
idx = max(range(n), key=lambda i: dists[i])
sx, sy = active[idx]
ux, uy = _unit(sx - com_x, sy - com_y)
tx, ty = sx + DELTA_COLLECT * ux, sy + DELTA_COLLECT * uy
mode = "collect"
else:
ux, uy = _unit(com_x - pen_target[0], com_y - pen_target[1])
tx, ty = com_x + DELTA_DRIVE * ux, com_y + DELTA_DRIVE * uy
mode = "drive"
ax, ay = _unit(tx - dog_xy[0], ty - dog_xy[1])
return ax, ay, mode, {
"n_active": n, "phase": mode, "radius": radius, "threshold": threshold,
"com_x": com_x, "com_y": com_y,
"target_x": tx, "target_y": ty,
}
herding/control/strombom.py
-37
View File
@@ -76,40 +76,3 @@ def compute_action(dog_xy, sheep_positions, pen_target=PEN_ENTRY):
ax, ay = _unit(tx - dog_xy[0], ty - dog_xy[1])
return ax, ay, mode
def compute_action_debug(dog_xy, sheep_positions, pen_target=PEN_ENTRY):
"""``compute_action`` plus a small debug dict (CoM, target, radius)."""
active = [(x, y) for (x, y) in sheep_positions.values() if _is_active(x, y)]
if not active:
return 0.0, 0.0, "idle", {
"n_active": 0, "radius": 0.0, "threshold": 0.0,
"com_x": 0.0, "com_y": 0.0,
"target_x": dog_xy[0], "target_y": dog_xy[1],
}
n = len(active)
com_x = sum(p[0] for p in active) / n
com_y = sum(p[1] for p in active) / n
dists = [math.hypot(p[0] - com_x, p[1] - com_y) for p in active]
radius = max(dists)
threshold = F_FACTOR * math.sqrt(n)
if radius > threshold:
idx = max(range(n), key=lambda i: dists[i])
sx, sy = active[idx]
ux, uy = _unit(sx - com_x, sy - com_y)
tx, ty = sx + DELTA_COLLECT * ux, sy + DELTA_COLLECT * uy
mode = "collect"
else:
ux, uy = _unit(com_x - pen_target[0], com_y - pen_target[1])
tx, ty = com_x + DELTA_DRIVE * ux, com_y + DELTA_DRIVE * uy
mode = "drive"
ax, ay = _unit(tx - dog_xy[0], ty - dog_xy[1])
dbg = {
"n_active": n, "radius": radius, "threshold": threshold,
"com_x": com_x, "com_y": com_y,
"target_x": tx, "target_y": ty,
}
return ax, ay, mode, dbg
herding/control/universal.py
-14
View File
@@ -207,17 +207,3 @@ def compute_action(dog_xy, dog_heading, sheep_positions,
omega = max(-1.0, min(1.0, OMEGA_GAIN * err / math.pi))
return ax, ay, omega, mode
def compute_action_diff(dog_xy, dog_heading, sheep_positions,
pen_target=PEN_ENTRY):
"""Compatibility wrapper returning ``(vx, vy, mode)`` — same as Strömbom.
Use this when plugging into existing differential-drive code that
doesn't expect omega.
"""
vx, vy, _omega, mode = compute_action(
dog_xy, dog_heading, sheep_positions, pen_target,
drive_mode="differential",
)
return vx, vy, mode