Johnny Fernandes
231 lines
8.4 KiB
Python
231 lines
8.4 KiB
Python
"""
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Shepherd Dog RL controller — runs a trained SB3 PPO policy inside Webots.
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Setup
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-----
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1. Copy your trained files into this directory:
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controllers/shepherd_dog_rl/best_model.zip
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controllers/shepherd_dog_rl/vecnorm.pkl
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2. In field.wbt, set the ShepherdDog robot's controller field to
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"shepherd_dog_rl". You can do this in the Webots GUI:
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click the robot → Controller → shepherd_dog_rl
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3. Optional: set controllerArgs to ["5"] (number of sheep) if it differs
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from the default of 5.
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The controller reads GPS (dog position) and Receiver (sheep broadcasts),
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builds the same 13-dim flock observation the training env used, normalises
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it with the saved VecNormalize stats, and converts the (vx, vy) policy
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output into differential wheel speeds.
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"""
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import sys
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import os
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import math
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import struct
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import numpy as np
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# ── make training code importable ───────────────────────────────────────────
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_HERE = os.path.dirname(os.path.abspath(__file__))
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_TRAINING = os.path.join(_HERE, "..", "..", "training")
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sys.path.insert(0, _TRAINING)
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from controller import Robot
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from stable_baselines3 import PPO
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from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
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from herding_env import HerdingEnv
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# ── constants (must match herding_env.py) ───────────────────────────────────
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FIELD = 15.0
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PEN_CENTER = np.array([11.5, -11.5], dtype=np.float32)
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PEN_X = (10.0, 13.0)
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PEN_Y = (-15.0, -8.0)
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DOG_SPEED = 2.5 # m/s
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WHEEL_R = 0.038 # wheel radius (metres) — from ShepherdDog.proto
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K_TURN = 4.0 # heading-error gain (rad/s per rad)
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EAR_AMPLITUDE = 0.35
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EAR_RATE = 8.0
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# ── model paths ─────────────────────────────────────────────────────────────
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MODEL_PATH = os.path.join(_HERE, "best_model.zip")
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VECNORM_PATH = os.path.join(_HERE, "vecnorm.pkl")
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def norm_angle(a: float) -> float:
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while a > math.pi: a -= 2 * math.pi
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while a < -math.pi: a += 2 * math.pi
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return a
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def in_pen(x: float, y: float) -> bool:
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return PEN_X[0] < x < PEN_X[1] and PEN_Y[0] < y < PEN_Y[1]
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def build_obs(dog_pos: np.ndarray,
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sheep_dict: dict,
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n_sheep: int) -> np.ndarray:
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"""
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Build the 13-dim flock observation — identical to HerdingEnv._obs().
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sheep_dict: {name: (x, y)} for ALL known sheep (penned or not).
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"""
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D = 2 * FIELD
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# Split active vs penned
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active_pos = np.array(
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[v for v in sheep_dict.values() if not in_pen(*v)],
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dtype=np.float32
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)
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n_active = len(active_pos)
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if n_active > 0:
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com = active_pos.mean(axis=0)
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d_from_com = np.linalg.norm(active_pos - com, axis=1)
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sorted_idx = np.argsort(d_from_com)[::-1]
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radius = float(d_from_com[sorted_idx[0]])
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def nth(n):
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return active_pos[sorted_idx[n]] if len(sorted_idx) > n else com
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far1, far2, far3 = nth(0), nth(1), nth(2)
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else:
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com = PEN_CENTER.copy()
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radius = 0.0
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far1 = far2 = far3 = PEN_CENTER.copy()
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frac_active = n_active / max(n_sheep, 1)
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return np.array([
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dog_pos[0] / FIELD, dog_pos[1] / FIELD,
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(com[0] - dog_pos[0]) / D, (com[1] - dog_pos[1]) / D,
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(far1[0] - com[0]) / D, (far1[1] - com[1]) / D,
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(far2[0] - com[0]) / D, (far2[1] - com[1]) / D,
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(far3[0] - com[0]) / D, (far3[1] - com[1]) / D,
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(PEN_CENTER[0] - com[0]) / D, (PEN_CENTER[1] - com[1]) / D,
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(PEN_CENTER[0] - far1[0]) / D, (PEN_CENTER[1] - far1[1]) / D,
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radius / D,
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frac_active,
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], dtype=np.float32)
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# ── Webots setup ─────────────────────────────────────────────────────────────
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robot = Robot()
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timestep = int(robot.getBasicTimeStep())
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# Drive motors
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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 = left_motor.getMaxVelocity()
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# Sensors
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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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# Cosmetic
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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")); right_ear.setPosition(float("inf"))
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left_ear.setVelocity(0.0); right_ear.setVelocity(0.0)
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ear_phase = 0.0
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# Number of sheep (from controllerArgs or default)
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try:
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n_sheep = int(sys.argv[1])
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except (IndexError, ValueError):
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n_sheep = 5
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# ── Load model ───────────────────────────────────────────────────────────────
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print(f"[RL dog] Loading model from {MODEL_PATH}")
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print(f"[RL dog] Loading vecnorm from {VECNORM_PATH}")
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dummy_env = DummyVecEnv([lambda: HerdingEnv(n_sheep=n_sheep)])
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vecnorm = VecNormalize.load(VECNORM_PATH, dummy_env)
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vecnorm.training = False
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vecnorm.norm_reward = False
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model = PPO.load(MODEL_PATH)
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print(f"[RL dog] Model loaded — running with n_sheep={n_sheep}")
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# ── Runtime state ─────────────────────────────────────────────────────────────
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sheep_positions: dict = {} # {name: (x, y)} — updated every step from receiver
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step_count = 0
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def bearing() -> float:
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"""Current robot heading in world frame (radians)."""
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n = compass.getValues()
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return math.atan2(n[0], n[1])
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def drive(action_vx: float, action_vy: float) -> None:
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"""Convert (vx, vy) policy action to differential wheel speeds."""
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speed_ms = math.sqrt(action_vx ** 2 + action_vy ** 2) * DOG_SPEED
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if speed_ms < 0.05:
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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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target_heading = math.atan2(action_vy, action_vx)
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err = norm_angle(target_heading - bearing())
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fwd_ms = speed_ms * max(0.0, math.cos(err))
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fwd_rad = fwd_ms / WHEEL_R
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turn = K_TURN * err # rad/s correction
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l = max(-MOTOR_MAX, min(MOTOR_MAX, fwd_rad - turn))
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r = max(-MOTOR_MAX, min(MOTOR_MAX, fwd_rad + turn))
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left_motor.setVelocity(l)
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right_motor.setVelocity(r)
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# ── Main loop ─────────────────────────────────────────────────────────────────
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while robot.step(timestep) != -1:
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step_count += 1
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# 1. Drain receiver — update sheep position table
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while receiver.getQueueLength() > 0:
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try:
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msg = receiver.getString()
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parts = msg.split(":")
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if parts[0] == "sheep" and len(parts) == 4:
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sheep_positions[parts[1]] = (float(parts[2]), float(parts[3]))
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except Exception:
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pass
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receiver.nextPacket()
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# 2. Dog GPS
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gps_vals = gps.getValues()
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dog_pos = np.array([gps_vals[0], gps_vals[1]], dtype=np.float32)
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# 3. Build and normalise observation
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raw_obs = build_obs(dog_pos, sheep_positions, n_sheep)
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obs_norm = vecnorm.normalize_obs(raw_obs[np.newaxis]) # (1, 13)
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# 4. Policy inference
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action, _ = model.predict(obs_norm, deterministic=True)
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vx, vy = float(action[0][0]), float(action[0][1])
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# 5. Drive
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drive(vx, vy)
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# 6. Broadcast dog position so sheep can compute flee forces
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emitter.send(f"dog:{dog_pos[0]:.4f}:{dog_pos[1]:.4f}")
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# 7. Ear animation
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ear_phase += 0.12
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ep = EAR_AMPLITUDE * math.sin(ear_phase)
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left_ear.setVelocity(EAR_RATE); right_ear.setVelocity(EAR_RATE)
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left_ear.setPosition( ep); right_ear.setPosition(-ep)
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# Periodic status
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if step_count % 100 == 0:
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n_in_pen = sum(1 for x, y in sheep_positions.values() if in_pen(x, y))
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print(f"[RL dog] step={step_count} known_sheep={len(sheep_positions)}"
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f" penned={n_in_pen}/{n_sheep}"
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f" action=({vx:.2f}, {vy:.2f})")
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