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Rename multi-segment functions to two-concept names; polish docstrings

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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>
This commit is contained in:
Johnny Fernandes
2026-05-17 01:58:15 +00:00
parent 10c01a938e
commit 7ab69ab0f3
14 changed files with 75 additions and 77 deletions
Download Patch File Download Diff File Expand all files Collapse all files
training/bc/pretrain.py
+3 -3
View File
@@ -55,7 +55,7 @@ def build_model(net_arch_pi, net_arch_vf, log_std_init: float,
return model, env
def policy_forward_mean(policy, obs_batch):
def forward_mean(policy, obs_batch):
"""Return the deterministic mean action for an obs batch.
SB3's ActorCriticPolicy routes ``forward`` through a Distribution
@@ -177,7 +177,7 @@ def main():
ob_batch = ob_batch.to(args.device)
act_batch = act_batch.to(args.device)
optimizer.zero_grad()
mean_action = policy_forward_mean(policy, ob_batch)
mean_action = forward_mean(policy, ob_batch)
loss, mse_val, cos_val = combined_loss(mean_action, act_batch)
loss.backward()
optimizer.step()
@@ -196,7 +196,7 @@ def main():
for ob_batch, act_batch in val_loader:
ob_batch = ob_batch.to(args.device)
act_batch = act_batch.to(args.device)
mean_action = policy_forward_mean(policy, ob_batch)
mean_action = forward_mean(policy, ob_batch)
bs = ob_batch.size(0)
val_total += nn.functional.mse_loss(
mean_action, act_batch, reduction="sum",
training/herding_env.py
+4 -4
View File
@@ -28,7 +28,7 @@ from gymnasium import spaces
from herding.world.diffdrive import (
heading_speed_to_wheels, kinematics_step,
mecanum_kinematics_step, velocity_to_mecanum_wheels, velocity_to_wheels,
mecanum_step, velocity_to_mecanum_wheels, velocity_to_wheels,
)
from herding.world.flocking_sim import (
FLEE_SPEED, MAX_SPEED, WANDER_SPEED, compute_heading_speed,
@@ -40,7 +40,7 @@ from herding.world.geometry import (
GATE_X, GATE_Y, MAX_SHEEP,
PEN_ENTRY, PEN_X, PEN_Y,
SHEEP_MAX_WHEEL_OMEGA, SHEEP_WHEEL_BASE, SHEEP_WHEEL_RADIUS,
WEBOTS_DT, clip_to_field, is_penned_position,
WEBOTS_DT, clip_to_field, is_penned,
)
from herding.perception.lidar_perception import detections_from_scan
from herding.perception.lidar_sim import simulate_scan
@@ -302,7 +302,7 @@ class HerdingEnv(gym.Env):
if robot_cfg is not None else 1.0)
strafe_bleed = (robot_cfg.strafe_to_forward_bleed
if robot_cfg is not None else 0.0)
self.dog_x, self.dog_y, self.dog_heading = mecanum_kinematics_step(
self.dog_x, self.dog_y, self.dog_heading = mecanum_step(
self.dog_x, self.dog_y, self.dog_heading,
w_fl, w_fr, w_rl, w_rr,
DOG_WHEEL_RADIUS,
@@ -337,7 +337,7 @@ class HerdingEnv(gym.Env):
self._step_one_sheep(i)
for i in range(self.n_sheep):
if (not self.sheep_penned[i]
and is_penned_position(self.sheep_x[i], self.sheep_y[i])):
and is_penned(self.sheep_x[i], self.sheep_y[i])):
self.sheep_penned[i] = True
# LiDAR perception runs after sheep move; feeds the obs and the