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TIR_PROJ/tests/test_config.py
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Johnny Fernandes 1c197e0ff7 Enable consensus tracker by default + round-world Strömbom fix 
Two changes that together raise diff/round gym success ~52%→88% (BC)
and ~68%→88% (RL) without retraining; diff/field stays at 100%.

* TrackerConfig.consensus_k default 1 → 3 (radius 0.5 m, max_age 15
  frames). The same candidate-promotion mechanism that closed the
  Webots LiDAR gap also filters gym tracker phantoms — they show up
  on the round field where sheep run further between detection
  cycles than GATE_M, so each new position spawns a fresh track
  while the stale one persists in memory. SheepTracker() called with
  no tracker_cfg keeps the legacy pass-through behaviour for
  backwards compatibility.
* Strömbom + universal teachers now detect when the natural
  "behind the flock" drive target leaves the curved boundary and
  fall back to pushing the flock radially inward toward the centre.
  Breaks the wall-circling pattern that previously trapped both the
  analytical baselines and the trained policies.

A/B numbers (n_sheep ∈ {1,2,3,5,10}, 5 seeds each, max_steps=15000):

  diff/field  bc:  baseline 100%  consensus 100%
  diff/field  rl:  baseline 100%  consensus 100%
  diff/round  bc:  baseline  52%  consensus  88%
  diff/round  rl:  baseline  68%  consensus  88%

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-16 21:09:25 +00:00

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"""Tests for herding/config.py — dataclass construction, defaults, overrides."""
import math
import pytest
from herding.config import (
DetectionConfig,
DomainRandomConfig,
HerdingConfig,
HERDING_DEFAULT,
HERDING_WEBOTS,
LidarConfig,
LIDAR_FULL,
LIDAR_WEBOTS,
RobotConfig,
TrackerConfig,
)
# ---------------------------------------------------------------------------
# LidarConfig
# ---------------------------------------------------------------------------
class TestLidarConfig:
def test_defaults_match_full_circle_preset(self):
assert LidarConfig() == LIDAR_FULL
def test_webots_preset(self):
assert LIDAR_WEBOTS.n_rays == 180
assert abs(LIDAR_WEBOTS.fov_rad - math.radians(140.0)) < 1e-9
def test_frozen(self):
cfg = LidarConfig()
with pytest.raises((AttributeError, TypeError)):
cfg.n_rays = 42 # type: ignore[misc]
def test_invalid_n_rays(self):
with pytest.raises(ValueError):
LidarConfig(n_rays=0)
def test_invalid_fov(self):
with pytest.raises(ValueError):
LidarConfig(fov_rad=0.0)
with pytest.raises(ValueError):
LidarConfig(fov_rad=math.pi * 3)
def test_invalid_max_range(self):
with pytest.raises(ValueError):
LidarConfig(max_range=-1.0)
# ---------------------------------------------------------------------------
# TrackerConfig
# ---------------------------------------------------------------------------
class TestTrackerConfig:
def test_defaults(self):
cfg = TrackerConfig()
assert cfg.forget_steps == 200
assert cfg.max_new_tracks_per_step == 10
def test_webots_preset_tighter(self):
cfg = HERDING_WEBOTS.tracker
# forget_steps was extended so confirmed sheep tracks survive
# sparse 140° FOV re-sightings; consensus blocks phantoms from
# reaching this lifetime.
assert cfg.forget_steps >= 200
assert cfg.max_new_tracks_per_step == 1
assert cfg.pen_latch_depth == 2.0
def test_default_consensus_enabled(self):
# Consensus is on by default — it filters tracker phantoms that
# confused the policy on the round field (52% → 88%) at no cost
# on the rectangular field (100% → 100%). Pass-through (k=1) is
# still available by explicitly constructing TrackerConfig(consensus_k=1).
cfg = TrackerConfig()
assert cfg.consensus_k >= 2
assert cfg.consensus_radius_m > 0.0
assert cfg.consensus_max_age > cfg.consensus_k
def test_webots_preset_enables_consensus(self):
cfg = HERDING_WEBOTS.tracker
assert cfg.consensus_k > 1
assert cfg.consensus_radius_m > 0.0
assert cfg.consensus_max_age >= cfg.consensus_k
def test_invalid_forget_steps(self):
with pytest.raises(ValueError):
TrackerConfig(forget_steps=0)
def test_invalid_max_new_tracks(self):
with pytest.raises(ValueError):
TrackerConfig(max_new_tracks_per_step=0)
def test_invalid_consensus_params(self):
with pytest.raises(ValueError):
TrackerConfig(consensus_k=0)
with pytest.raises(ValueError):
TrackerConfig(consensus_radius_m=0.0)
with pytest.raises(ValueError):
TrackerConfig(consensus_max_age=0)
# ---------------------------------------------------------------------------
# DetectionConfig
# ---------------------------------------------------------------------------
class TestDetectionConfig:
def test_defaults(self):
cfg = DetectionConfig()
assert cfg.wall_reject == 0.5
def test_webots_preset_wall_reject(self):
# wall_reject stays at 0.5 m — 1.0 m was too aggressive near the south gate
cfg = HERDING_WEBOTS.detection
assert cfg.wall_reject == 0.5
def test_invalid_wall_reject(self):
with pytest.raises(ValueError):
DetectionConfig(wall_reject=-0.1)
# ---------------------------------------------------------------------------
# RobotConfig
# ---------------------------------------------------------------------------
class TestRobotConfig:
def test_max_linear_derived(self):
cfg = RobotConfig()
assert abs(cfg.max_linear - cfg.wheel_radius * cfg.max_wheel_omega) < 1e-9
def test_default_action_smooth_zero(self):
assert RobotConfig().action_smooth == 0.0
def test_webots_action_smooth(self):
assert HERDING_WEBOTS.robot.action_smooth == 0.55
def test_invalid_action_smooth(self):
with pytest.raises(ValueError):
RobotConfig(action_smooth=1.0)
with pytest.raises(ValueError):
RobotConfig(action_smooth=-0.1)
# ---------------------------------------------------------------------------
# DomainRandomConfig
# ---------------------------------------------------------------------------
class TestDomainRandomConfig:
def test_all_zeros_by_default(self):
cfg = DomainRandomConfig()
assert cfg.fp_rate == 0.0
assert cfg.wheel_slip_std == 0.0
assert cfg.compass_noise_std == 0.0
def test_invalid_fp_rate(self):
with pytest.raises(ValueError):
DomainRandomConfig(fp_rate=-1.0)
def test_invalid_slip_std(self):
with pytest.raises(ValueError):
DomainRandomConfig(wheel_slip_std=-0.01)
# ---------------------------------------------------------------------------
# HerdingConfig
# ---------------------------------------------------------------------------
class TestHerdingConfig:
def test_default_is_herding_default(self):
assert HerdingConfig() == HERDING_DEFAULT
def test_replace_sub_config(self):
new_cfg = HERDING_WEBOTS.replace(
domain_random=DomainRandomConfig(fp_rate=2.0)
)
assert new_cfg.domain_random.fp_rate == 2.0
# Other sub-configs unchanged
assert new_cfg.tracker == HERDING_WEBOTS.tracker
assert new_cfg.lidar == HERDING_WEBOTS.lidar
def test_herding_default_matches_original_module_constants(self):
"""Verify the default config reproduces the original hardcoded values."""
from herding.perception.lidar_sim import (
LIDAR_N_RAYS, LIDAR_FOV, LIDAR_MAX_RANGE, LIDAR_NOISE,
SHEEP_RADIUS, POST_RADIUS,
)
from herding.perception.lidar_perception import (
GAP_THRESHOLD, MAX_CLUSTER_SPAN, RANGE_HIT_EPS,
SPLIT_RANGE_GAP, WALL_REJECT, STATIC_REJECT,
)
from herding.perception.sheep_tracker import (
GATE_M, REACQUIRE_GATE_M, REACQUIRE_MIN_AGE, PENNED_GATE_M,
FORGET_STEPS, PREDICT_STEPS, VELOCITY_CLAMP,
)
cfg = HERDING_DEFAULT
assert cfg.lidar.n_rays == LIDAR_N_RAYS
assert cfg.lidar.fov_rad == LIDAR_FOV
assert cfg.lidar.max_range == LIDAR_MAX_RANGE
assert cfg.lidar.noise_std == LIDAR_NOISE
assert cfg.lidar.sheep_radius == SHEEP_RADIUS
assert cfg.lidar.post_radius == POST_RADIUS
assert cfg.detection.gap_threshold == GAP_THRESHOLD
assert cfg.detection.max_cluster_span == MAX_CLUSTER_SPAN
assert cfg.detection.range_hit_eps == RANGE_HIT_EPS
assert cfg.detection.split_range_gap == SPLIT_RANGE_GAP
assert cfg.detection.wall_reject == WALL_REJECT
assert cfg.detection.static_reject == STATIC_REJECT
assert cfg.tracker.gate_m == GATE_M
assert cfg.tracker.reacquire_gate_m == REACQUIRE_GATE_M
assert cfg.tracker.reacquire_min_age == REACQUIRE_MIN_AGE
assert cfg.tracker.penned_gate_m == PENNED_GATE_M
assert cfg.tracker.forget_steps == FORGET_STEPS
assert cfg.tracker.predict_steps == PREDICT_STEPS
assert cfg.tracker.velocity_clamp == VELOCITY_CLAMP
# ---------------------------------------------------------------------------
# Integration: HerdingEnv honours the config
# ---------------------------------------------------------------------------
class TestHerdingEnvConfig:
def test_default_env_unchanged(self):
"""HerdingEnv() still works with no config — zero behaviour change."""
from training.herding_env import HerdingEnv
env = HerdingEnv(n_sheep=1, max_steps=5, difficulty=1.0, seed=0)
obs, info = env.reset()
assert obs.shape == (32,)
obs2, *_ = env.step(env.action_space.sample())
assert obs2.shape == (32,)
def test_webots_config_propagates_action_smooth(self):
from training.herding_env import HerdingEnv
env = HerdingEnv(herding_cfg=HERDING_WEBOTS)
assert env.ACTION_SMOOTH == 0.55
def test_webots_config_runs(self):
from training.herding_env import HerdingEnv
env = HerdingEnv(
n_sheep=2, max_steps=10, difficulty=1.0, seed=42,
herding_cfg=HERDING_WEBOTS,
)
obs, _ = env.reset()
for _ in range(5):
obs, _, terminated, truncated, _ = env.step(env.action_space.sample())
assert obs.shape == (32,)
def test_domain_random_fp_runs(self):
from training.herding_env import HerdingEnv
cfg = HERDING_WEBOTS.replace(
domain_random=DomainRandomConfig(fp_rate=3.0, fp_std_pos=0.2)
)
env = HerdingEnv(n_sheep=2, max_steps=10, difficulty=1.0, seed=7, herding_cfg=cfg)
env.reset()
for _ in range(5):
env.step(env.action_space.sample())
def test_domain_random_slip_runs(self):
from training.herding_env import HerdingEnv
cfg = HERDING_WEBOTS.replace(
domain_random=DomainRandomConfig(wheel_slip_std=0.05, compass_noise_std=0.02)
)
env = HerdingEnv(n_sheep=1, max_steps=10, difficulty=1.0, seed=3,
drive_mode="mecanum", herding_cfg=cfg)
env.reset()
for _ in range(5):
env.step(env.action_space.sample())
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