Johnny Fernandes
329 lines
12 KiB
Python
329 lines
12 KiB
Python
"""Shepherd Dog controller (Webots).
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Mode is selected by ``HERDING_MODE`` (env var, or via the
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``herding_runtime.cfg`` file the launcher writes since Webots strips
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env vars on some setups):
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strombom → canonical Strömbom (2014) collect/drive heuristic
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wrapped in ActiveScanTeacher (opening rotation +
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walk-to-centre when the tracker briefly empties).
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sequential → single-target "pin-and-push", same wrapper.
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bc → behaviour-cloned MLP, trained on Strömbom demos.
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Default policy: training/runs/bc/policy.zip.
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rl → KL-regularised PPO fine-tune of bc. Same obs/action
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space as bc; refines time-to-pen via reward while
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staying anchored to bc.
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Default policy: training/runs/rl/policy.zip.
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Sheep perception
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----------------
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The dog perceives sheep through its **front-mounted 140° LiDAR**
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(``protos/ShepherdDog.proto``: 180 rays, 12 m max range). Each step:
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1. Reads ``lidar.getRangeImage()``.
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2. Runs ``herding.perception.lidar_perception.detections_from_scan``
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to cluster returns into world-frame ``(x, y)`` sheep estimates.
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3. Folds those into a ``SheepTracker`` which maintains last-seen
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positions for sheep currently out of FOV and latches "penned"
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once a track crosses the gate plane south.
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Sheep ``emitter`` messages are read **for diagnostic logging only**
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(GT_penned counter + auto-finish sentinel); they are never used to
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drive the policy. Perception for control comes entirely from LiDAR.
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Auto-finish
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-----------
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When the dog observes (via GT, read off the receiver) that all sheep
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are penned, it writes ``training/.run_done`` and the launcher
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(``tools/run_webots.sh``) detects it and closes Webots. This keeps
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batch evaluation runs bounded.
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"""
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import math
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import os
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import sys
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# --- Make the shared herding/ package importable from this controller dir ---
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_HERE = os.path.dirname(os.path.abspath(__file__))
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_PROJECT_ROOT = os.path.normpath(os.path.join(_HERE, "..", ".."))
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if _PROJECT_ROOT not in sys.path:
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sys.path.insert(0, _PROJECT_ROOT)
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import numpy as np
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from controller import Robot
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from herding.control.active_scan import ActiveScanTeacher
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from herding.control.modulation import modulate_speed_near_sheep
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from herding.control.sequential import compute_action as sequential_action
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from herding.control.strombom import compute_action as strombom_action
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from herding.perception.obs import build_obs
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from herding.perception.lidar_perception import detections_from_scan
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from herding.perception.sheep_tracker import SheepTracker
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from herding.world.diffdrive import velocity_to_wheels
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from herding.world.geometry import (
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DOG_MAX_LINEAR, DOG_MAX_WHEEL_OMEGA,
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DOG_SOUTH_LIMIT, DOG_WHEEL_RADIUS,
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PEN_ENTRY, is_penned_position,
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)
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# ---------------------------------------------------------------------------
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# Mode + policy resolution
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# ---------------------------------------------------------------------------
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def _load_runtime_config():
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"""Read mode + policy_dir overrides from a runtime config file.
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Webots strips HERDING_* env vars in some configurations, so the
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launcher writes a tiny ``herding_runtime.cfg`` (key=value lines)
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in the project root and the controller reads it here. Env vars
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win if both are present; the file is the fallback.
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"""
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cfg_path = os.path.join(_PROJECT_ROOT, "herding_runtime.cfg")
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if not os.path.exists(cfg_path):
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return {}
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out = {}
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try:
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with open(cfg_path) as f:
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for line in f:
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line = line.strip()
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if not line or line.startswith("#") or "=" not in line:
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continue
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k, _, v = line.partition("=")
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out[k.strip().upper()] = v.strip()
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except OSError:
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return {}
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return out
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_runtime_cfg = _load_runtime_config()
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MODE = (os.environ.get("HERDING_MODE")
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or _runtime_cfg.get("HERDING_MODE")
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or "bc").lower()
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def _resolve_policy_dir(mode: str) -> str:
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"""Where to look for the trained policy for the given mode.
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Priority:
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1. HERDING_POLICY_DIR env var or runtime-cfg entry, if it points
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to a real directory.
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2. Mode-specific default:
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bc → training/runs/bc (Strömbom-imitated MLP)
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rl → training/runs/rl (KL-PPO fine-tune of bc)
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3. Fall back to bc.
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All checkpoints are frame-stacked K = 4; ``policy_loader`` reads
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the stacking factor from the policy's observation space.
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"""
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env_dir = (os.environ.get("HERDING_POLICY_DIR")
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or _runtime_cfg.get("HERDING_POLICY_DIR"))
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if env_dir and os.path.isdir(env_dir):
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return env_dir
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mode_default = {
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"bc": os.path.join(_PROJECT_ROOT, "training", "runs", "bc"),
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"rl": os.path.join(_PROJECT_ROOT, "training", "runs", "rl"),
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}
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primary = mode_default.get(mode, mode_default["bc"])
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if os.path.isdir(primary):
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return primary
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fallback = mode_default["bc"]
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if os.path.isdir(fallback):
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return fallback
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return env_dir or primary
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_VALID_MODES = ("bc", "rl", "strombom", "sequential")
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if MODE not in _VALID_MODES:
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print(f"[dog] unknown HERDING_MODE={MODE!r}; defaulting to strombom.")
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MODE = "strombom"
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POLICY_DIR = _resolve_policy_dir(MODE)
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policy_handle = None
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if MODE in ("bc", "rl"):
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print(f"[dog] resolved POLICY_DIR={POLICY_DIR} exists={os.path.isdir(POLICY_DIR)}")
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try:
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from policy_loader import load as _load_policy
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policy_handle = _load_policy(POLICY_DIR)
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print(f"[dog] policy loaded from {POLICY_DIR}")
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except Exception as exc:
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print(f"[dog] policy load failed ({exc!r}); falling back to strombom.")
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MODE = "strombom"
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print(f"[dog] running in mode={MODE}")
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# ---------------------------------------------------------------------------
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# Control parameters
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# ---------------------------------------------------------------------------
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ACTION_SMOOTH = 0.55 # EMA on (vx, vy) — kills frame-to-frame jitter
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RUN_DONE_FILE = os.path.join(_PROJECT_ROOT, "training", ".run_done")
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def safety_clamp(vx: float, vy: float, dog_x: float, dog_y: float) -> tuple:
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"""If the dog is near the south barrier and the action would push it
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further south, override with a northward action. Hard invariant: the
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dog never enters the pen."""
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if dog_y < DOG_SOUTH_LIMIT and vy < 0.0:
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return (0.0, 1.0)
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if dog_y < DOG_SOUTH_LIMIT + 0.5 and vy < -0.2:
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return (vx * 0.5, max(0.0, vy + 0.5))
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return (vx, vy)
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def drive(vx: float, vy: float, left_motor, right_motor, compass, motor_max: float):
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if math.hypot(vx, vy) < 1e-3:
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left_motor.setVelocity(0.0)
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right_motor.setVelocity(0.0)
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return
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n = compass.getValues()
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h = math.atan2(n[0], n[1])
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left, right = velocity_to_wheels(
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vx, vy, h,
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max_linear=DOG_MAX_LINEAR,
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wheel_radius=DOG_WHEEL_RADIUS,
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max_wheel_omega=motor_max,
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k_turn=4.0,
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)
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left_motor.setVelocity(left)
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right_motor.setVelocity(right)
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# ---------------------------------------------------------------------------
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# Webots devices
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# ---------------------------------------------------------------------------
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robot = Robot()
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timestep = int(robot.getBasicTimeStep())
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left_motor = robot.getDevice("left wheel motor")
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right_motor = robot.getDevice("right wheel motor")
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left_motor.setPosition(float("inf"))
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right_motor.setPosition(float("inf"))
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left_motor.setVelocity(0.0)
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right_motor.setVelocity(0.0)
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MOTOR_MAX = min(left_motor.getMaxVelocity(), DOG_MAX_WHEEL_OMEGA)
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gps = robot.getDevice("gps"); gps.enable(timestep)
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compass = robot.getDevice("compass"); compass.enable(timestep)
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receiver = robot.getDevice("receiver"); receiver.enable(timestep)
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emitter = robot.getDevice("emitter")
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lidar = robot.getDevice("lidar"); lidar.enable(timestep)
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tracker = SheepTracker()
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# Cosmetic ear motors — animated; not used by control.
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left_ear = robot.getDevice("left ear motor")
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right_ear = robot.getDevice("right ear motor")
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left_ear.setPosition(float("inf"))
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right_ear.setPosition(float("inf"))
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left_ear.setVelocity(0.0)
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right_ear.setVelocity(0.0)
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ear_phase = 0.0
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EAR_AMPLITUDE = 0.35
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EAR_RATE = 8.0
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# ---------------------------------------------------------------------------
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# Main loop
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# ---------------------------------------------------------------------------
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# Analytic-teacher wrapper (instantiated lazily so RL/BC modes don't pay
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# the import-time cost). Each gets the same ActiveScanTeacher treatment:
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# rotate-on-empty, walk-to-centre, near-sheep speed modulation.
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analytic_teacher = None
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if MODE in ("strombom", "sequential"):
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base_fn = strombom_action if MODE == "strombom" else sequential_action
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analytic_teacher = ActiveScanTeacher(base_fn)
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# GT positions from sheep emitters — used **only** for the auto-finish
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# sentinel and the GT_penned diagnostic line. Never fed into control.
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_gt_sheep: dict = {}
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_run_done = False
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prev_action = (0.0, 0.0)
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step_count = 0
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while robot.step(timestep) != -1:
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step_count += 1
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# Drain sheep emitter messages → GT (diagnostic only).
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while receiver.getQueueLength() > 0:
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msg = receiver.getString()
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receiver.nextPacket()
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parts = msg.split(":")
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if len(parts) == 4 and parts[0] == "sheep":
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try:
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_gt_sheep[parts[1]] = (float(parts[2]), float(parts[3]))
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except ValueError:
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pass
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pos = gps.getValues()
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dog_xy = (pos[0], pos[1])
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n = compass.getValues()
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dog_heading = math.atan2(n[0], n[1])
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# ---- LiDAR perception → tracker → active sheep positions ----
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ranges = np.asarray(lidar.getRangeImage(), dtype=np.float32)
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detections = detections_from_scan(ranges, dog_xy[0], dog_xy[1], dog_heading)
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sheep_positions = tracker.update(detections)
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sheep_xy_list = list(sheep_positions.values())
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sheep_penned_list = [False] * len(sheep_xy_list)
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single_obs = build_obs(dog_xy, dog_heading, sheep_xy_list, sheep_penned_list)
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# ---- Action selection ----
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if MODE in ("bc", "rl") and policy_handle is not None:
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action = policy_handle.predict(single_obs)
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vx, vy = float(action[0]), float(action[1])
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else:
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vx, vy, _mode_str = analytic_teacher(
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dog_xy, dog_heading, sheep_positions, PEN_ENTRY,
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)
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# Near-sheep speed modulation (shared by every mode).
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vx, vy = modulate_speed_near_sheep(vx, vy, dog_xy, sheep_positions)
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# EMA smoothing — kills frame-to-frame action jitter.
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vx = ACTION_SMOOTH * prev_action[0] + (1.0 - ACTION_SMOOTH) * vx
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vy = ACTION_SMOOTH * prev_action[1] + (1.0 - ACTION_SMOOTH) * vy
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# Safety: dog must never enter the pen.
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vx, vy = safety_clamp(vx, vy, dog_xy[0], dog_xy[1])
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prev_action = (vx, vy)
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drive(vx, vy, left_motor, right_motor, compass, MOTOR_MAX)
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emitter.send(f"dog:{dog_xy[0]:.4f}:{dog_xy[1]:.4f}")
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# Cosmetic ear wiggle.
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ear_phase += 0.12
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ear_pos = EAR_AMPLITUDE * math.sin(ear_phase)
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left_ear.setVelocity(EAR_RATE)
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right_ear.setVelocity(EAR_RATE)
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left_ear.setPosition(ear_pos)
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right_ear.setPosition(-ear_pos)
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# Auto-finish: when all GT sheep are penned, write the sentinel.
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# The launcher polls for it and closes Webots so batch evals don't
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# hang after the task is done. Bounded by `_gt_sheep` so we don't
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# fire during the first few steps while the receiver fills.
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if _gt_sheep and not _run_done:
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gt_active = sum(1 for x, y in _gt_sheep.values()
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if not is_penned_position(x, y))
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if gt_active == 0:
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os.makedirs(os.path.dirname(RUN_DONE_FILE), exist_ok=True)
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open(RUN_DONE_FILE, "w").close()
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_run_done = True
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print(f"[dog] all {len(_gt_sheep)} sheep penned at step "
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f"{step_count} — wrote sentinel, launcher will close Webots")
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if step_count % 200 == 0:
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gt_penned = sum(1 for x, y in _gt_sheep.values()
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if is_penned_position(x, y))
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gt_total = len(_gt_sheep)
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print(f"[dog mode={MODE}] step={step_count} "
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f"GT_penned={gt_penned}/{gt_total} "
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f"tracks_active={tracker.n_active()} "
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f"tracks_penned={tracker.n_penned()} "
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f"detections={len(detections)} action=({vx:+.2f}, {vy:+.2f})")
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