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85 lines
3.1 KiB
Python
85 lines
3.1 KiB
Python
"""Optional navigation-only behavior-cloning initialization, followed by PPO.
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The teacher is used ONLY to collect training data. Export contains the trained MLP,
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not a hidden scripted navigation fallback. Later stages never use this initializer.
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"""
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import math
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import mujoco
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import numpy as np
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import torch
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from .env import MobileManipulatorEnv
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from .kernel import TASK, navigation_error
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def navigation_teacher(state):
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_, yaw = navigation_error(state)
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x = state[37] - TASK["navigationOffset"][0] - state[0]
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y = state[38] - TASK["navigationOffset"][1] - state[1]
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action = np.zeros(TASK["actionSize"], dtype=np.float32)
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action[0] = 8 * (math.cos(yaw) * x + math.sin(yaw) * y)
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action[1] = 8 * (-math.sin(yaw) * x + math.cos(yaw) * y)
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action[2] = -4 * yaw
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return np.clip(action, -1, 1)
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def bootstrap_navigation(agent, package, params, reset_options, seed):
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steps = params["navigationBootstrapSteps"]
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if not steps:
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return
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print(f"Navigation BC initialization: {steps} simulated teacher steps, then PPO", flush=True)
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env = MobileManipulatorEnv(
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package,
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stage="navigate",
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reset_options=reset_options,
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position_jitter=params["positionJitter"],
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)
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rng = np.random.default_rng(seed)
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observations, actions = [], []
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def reset():
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env.reset()
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# Cover heading errors rather than cloning only a straight, zero-yaw path.
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yaw = rng.uniform(-0.6, 0.6)
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env.data.qpos[env.base_q + 3 : env.base_q + 7] = [
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math.cos(yaw / 2),
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0,
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0,
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math.sin(yaw / 2),
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]
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mujoco.mj_forward(env.model, env.data)
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env.hold()
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return env.observe().copy()
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try:
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env.reset(seed=seed)
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observation = reset()
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for _ in range(steps):
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action = navigation_teacher(env.state)
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observations.append(observation.copy())
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actions.append(action)
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# Cover nearby off-teacher states without teleportation or privileged deployment inputs.
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noisy = action.copy()
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noisy[:3] += rng.normal(0, 0.2, 3)
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observation, _, terminated, truncated, _ = env.step(noisy)
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if terminated or truncated:
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observation = reset()
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finally:
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env.close()
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x = torch.as_tensor(np.asarray(observations), device=agent.device)
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y = torch.as_tensor(np.asarray(actions), device=agent.device)
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policy = agent.policy
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optimizer = torch.optim.Adam(policy.parameters(), lr=1e-3)
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for _ in range(30):
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for batch in torch.randperm(steps, device=agent.device).split(256):
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features = policy.extract_features(x[batch])
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predicted = policy.action_net(policy.mlp_extractor.forward_actor(features))
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loss = torch.nn.functional.mse_loss(predicted, y[batch])
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optimizer.zero_grad()
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loss.backward()
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torch.nn.utils.clip_grad_norm_(policy.parameters(), 1)
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optimizer.step()
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print(f"Navigation BC initialization finished; final batch MSE={loss.item():.6g}", flush=True)
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