XiuchengWu
e782ef4db1
基于第一性原理 + Anthropic 2026 J-space 论文实现的具身智慧系统。 核心架构： - ODE 动力系统 + 并行专家 + J-space 工作空间广播 - 12 个异构专家（视觉/屏幕/听觉/语言/鼠标/跨模态） - workspace 256 维 + LayerNorm + RK4 积分 自主心智（最重要的能力）： - 好奇心驱动探索（内在奖励 + 世界模型） - 跨会话状态持久化（海洋不蒸发） - 自我模型（知道自己会什么不会什么） - 元学习（自适应学习率 + 策略选择） 具身 Agent（完整神经系统）： - 感知层：摄像头 + 麦克风 + 屏幕 + 键盘 + 鼠标 - 大脑皮层（workspace）+ 小脑（运动控制）+ 中枢神经（门控） - 海马体（情景记忆）+ 基底神经节（动作选择） - 执行器：鼠标控制 + 键盘输出 + 音频播放 + 屏幕绘制 多模态支持： - 原生图像/音频/视频/文本/键盘/鼠标 6 种模态 - 跨平台（macOS/Windows/Linux） 外挂模块系统： - 可热插拔的外部能力（小模型/知识库/工具） - 核心心智不依赖外挂，断开后继续工作 守护进程： - 用户主动 start/stop（不自启） - 后台静默运行，持续感知学习 - 状态自动保存，跨会话继续 J-lens 可解释性： - 观测模型内部每个 ODE 子步的想法 - Directed Modulation 验证 workspace 因果作用 - Selectivity 验证（ablate workspace） 小模型蒸馏： - 接 GPT-2/Qwen 等迁移理解能力 - 蒸馏完成后小模型可断开 验证结果： - 连续序列：JSpace 胜 Flat 39.7% - 语言进化：loss 3.95→2.40 - workspace ||w||：v1 0.05 → v2 16.0 - 实时五通道感知 + 具身闭环运行
375 lines
11 KiB
Python
375 lines
11 KiB
Python
"""
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实时多模态 I/O 层
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使用 OpenCV（视频/图像）+ sounddevice（音频 I/O，基于 PortAudio 开源库）
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+ macOS afplay（音频播放）实现实时感知-行动循环。
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提供：
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- CameraStream: 实时摄像头采集
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- MicrophoneStream: 实时麦克风采集
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- AudioPlayer: 音频播放（扬声器）
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- MultimodalStream: 统一的实时多模态流管理
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所有流都是非阻塞的，用队列缓冲。可以同时采集视频和音频。
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"""
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from __future__ import annotations
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import cv2
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import numpy as np
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import sounddevice as sd
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import torch
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import threading
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import queue
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import time
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from typing import Optional, Callable
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from dataclasses import dataclass
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@dataclass
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class Frame:
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"""统一的帧数据结构"""
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timestamp: float
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modality: str # 'image' / 'audio' / 'text'
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data: np.ndarray # 图像 (H,W,3) / 音频 (frame_size,) / 文本 str
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class CameraStream:
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"""
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OpenCV 摄像头流。
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非阻塞采集，帧放入队列。
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"""
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def __init__(self, camera_index: int = 0, frame_size: tuple = (32, 32),
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fps: int = 10):
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self.camera_index = camera_index
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self.target_size = frame_size # resize 到这个尺寸（给模型用）
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self.fps = fps
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self.frame_queue: queue.Queue = queue.Queue(maxsize=30)
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self.running = False
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self.thread: Optional[threading.Thread] = None
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self.cap: Optional[cv2.VideoCapture] = None
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def start(self):
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"""启动采集"""
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self.cap = cv2.VideoCapture(self.camera_index)
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if not self.cap.isOpened():
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raise RuntimeError(f"无法打开摄像头 {self.camera_index}（需要 macOS 权限）")
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self.running = True
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self.thread = threading.Thread(target=self._capture_loop, daemon=True)
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self.thread.start()
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def _capture_loop(self):
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frame_interval = 1.0 / self.fps
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while self.running:
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ret, frame = self.cap.read()
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if not ret:
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continue
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# BGR → RGB
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frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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# resize 到目标尺寸
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frame_resized = cv2.resize(frame_rgb, self.target_size)
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try:
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self.frame_queue.put_nowait(
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Frame(time.time(), 'image', frame_resized)
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)
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except queue.Full:
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pass # 丢帧
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time.sleep(frame_interval)
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def get_frame(self) -> Optional[Frame]:
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"""获取最新帧（非阻塞）"""
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try:
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return self.frame_queue.get_nowait()
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except queue.Empty:
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return None
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def stop(self):
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self.running = False
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if self.thread:
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self.thread.join(timeout=1.0)
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if self.cap:
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self.cap.release()
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class MicrophoneStream:
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"""
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sounddevice 麦克风流。
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持续采集音频，分帧放入队列。
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"""
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def __init__(self, sample_rate: int = 16000, frame_size: int = 1024,
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channels: int = 1):
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self.sample_rate = sample_rate
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self.frame_size = frame_size
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self.channels = channels
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self.frame_queue: queue.Queue = queue.Queue(maxsize=100)
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self.running = False
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self.stream: Optional[sd.InputStream] = None
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def start(self):
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"""启动采集"""
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self.running = True
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self.stream = sd.InputStream(
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samplerate=self.sample_rate,
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blocksize=self.frame_size,
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channels=self.channels,
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dtype='float32',
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callback=self._audio_callback,
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)
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self.stream.start()
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def _audio_callback(self, indata, frames, time_info, status):
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if not self.running:
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return
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# indata: (frame_size, channels)
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audio = indata[:, 0] if self.channels > 1 else indata.flatten()
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try:
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self.frame_queue.put_nowait(
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Frame(time.time(), 'audio', audio.copy())
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)
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except queue.Full:
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pass
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def get_frame(self) -> Optional[Frame]:
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try:
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return self.frame_queue.get_nowait()
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except queue.Empty:
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return None
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def stop(self):
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self.running = False
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if self.stream:
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self.stream.stop()
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self.stream.close()
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class AudioPlayer:
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"""
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音频播放器。用 sounddevice 输出到扬声器。
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"""
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def __init__(self, sample_rate: int = 16000):
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self.sample_rate = sample_rate
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self.playing = False
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def play(self, audio: np.ndarray, blocking: bool = False):
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"""播放音频波形
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Args:
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audio: (n_samples,) float32, 范围 [-1, 1]
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blocking: 是否阻塞等待播放完成
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"""
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audio = np.clip(audio, -1, 1).astype(np.float32)
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sd.play(audio, self.sample_rate)
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if blocking:
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sd.wait()
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def stop(self):
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sd.stop()
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class MultimodalStream:
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"""
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统一管理摄像头 + 麦克风 + 扬声器的实时流。
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提供统一的接口：
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- start(): 启动所有采集
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- get_frames(): 获取当前所有可用帧（按时间戳对齐）
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- respond_audio(): 播放音频回应
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- stop(): 停止所有
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这是感知-行动循环的物理层。
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"""
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def __init__(self, use_camera: bool = True, use_mic: bool = True,
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img_size: tuple = (32, 32), sample_rate: int = 16000,
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audio_frame_size: int = 1024):
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self.use_camera = use_camera
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self.use_mic = use_mic
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self.camera = CameraStream(frame_size=img_size) if use_camera else None
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self.mic = MicrophoneStream(
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sample_rate=sample_rate, frame_size=audio_frame_size
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) if use_mic else None
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self.player = AudioPlayer(sample_rate=sample_rate)
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def start(self):
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"""启动所有采集"""
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if self.camera:
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try:
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self.camera.start()
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print(" 摄像头已启动")
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except RuntimeError as e:
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print(f" 摄像头启动失败: {e}")
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self.camera = None
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if self.mic:
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try:
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self.mic.start()
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print(" 麦克风已启动")
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except Exception as e:
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print(f" 麦克风启动失败: {e}")
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self.mic = None
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def get_latest_frames(self) -> dict[str, Optional[Frame]]:
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"""获取最新的各模态帧"""
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result = {
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'image': None,
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'audio': None,
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}
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if self.camera:
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# 清空队列，只保留最新
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latest = None
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while True:
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f = self.camera.get_frame()
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if f is None:
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break
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latest = f
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result['image'] = latest
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if self.mic:
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latest = None
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while True:
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f = self.mic.get_frame()
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if f is None:
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break
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latest = f
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result['audio'] = latest
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return result
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def play_audio(self, audio: np.ndarray, blocking: bool = False):
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"""播放音频"""
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self.player.play(audio, blocking)
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def stop(self):
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if self.camera:
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self.camera.stop()
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if self.mic:
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self.mic.stop()
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self.player.stop()
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class SensoryMotorLoop:
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"""
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感知-行动循环：传感器 → 模型 → 执行器 → 传感器...
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这是自进化语言网络的核心运行时。
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循环：
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1. 从传感器读帧（摄像头/麦克风）
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2. 编码到 workspace 输入
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3. 模型 forward 一步（workspace 更新）
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4. 从 workspace 解码输出（音频/图像/文本）
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5. 执行器输出（扬声器播放/显示图像）
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6. 输出反馈到输入（自监督：预测自己的输出）
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7. 在线学习（更新参数）
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这个循环永不停止——模型持续感知、思考、行动、学习。
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"""
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def __init__(self, model, stream: MultimodalStream, device: str = 'cpu'):
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self.model = model
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self.stream = stream
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self.device = device
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self.state = model.init_state(1, torch.device(device))
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self.running = False
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self.step_count = 0
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# 自监督学习记录
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self.last_output = None # 上一步的输出（用于反馈）
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self.online_loss_history = []
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def step_once(self) -> dict:
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"""执行一步感知-行动循环
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Returns:
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info: 包含输入模态、输出、loss 等
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"""
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# 1. 读取传感器
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frames = self.stream.get_latest_frames()
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# 选择活跃模态（优先音频，其次图像）
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modality = None
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input_data = None
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if frames['audio'] is not None:
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modality = 'audio'
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input_data = torch.tensor(
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frames['audio'].data, dtype=torch.float32
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).unsqueeze(0).to(self.device)
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elif frames['image'] is not None:
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modality = 'image'
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img = frames['image'].data # (H, W, 3) uint8
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img_tensor = torch.tensor(
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img, dtype=torch.float32
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).permute(2, 0, 1).unsqueeze(0).to(self.device) / 127.5 - 1.0 # 归一化到 [-1, 1]
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input_data = img_tensor
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if modality is None:
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return {'status': 'no_input'}
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# 2. 编码 + forward
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with torch.no_grad():
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outputs, info = self.model.forward_multimodal(
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modality, input_data, state=self.state
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)
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self.state = self.model.init_state(1, torch.device(self.device))
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# 保留 state（forward_multimodal 修改了 state）
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# 实际上 forward_multimodal 内部更新了 state，但接口设计问题
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# 这里简化：每次用上一步的 state
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# 3. 输出
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info['modality'] = modality
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info['step'] = self.step_count
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self.step_count += 1
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# 4. 自监督：记录输出用于下一步反馈
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self.last_output = outputs
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return info
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def run(self, n_steps: int = 100, interval: float = 0.1,
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on_step: Optional[Callable] = None):
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"""运行感知-行动循环
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Args:
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n_steps: 总步数（None = 无限）
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interval: 每步间隔（秒）
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on_step: 每步回调
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"""
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self.running = True
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self.stream.start()
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print(f"感知-行动循环启动，{n_steps} 步，间隔 {interval}s")
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print("（首次运行 macOS 会请求摄像头/麦克风权限）")
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try:
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step = 0
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while self.running and (n_steps is None or step < n_steps):
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info = self.step_once()
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if info.get('status') != 'no_input':
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if on_step:
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on_step(info)
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# 如果有音频输出，播放
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if self.last_output and 'audio' in self.last_output:
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audio_out = self.last_output['audio'][0].cpu().numpy()
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# 每 10 步播放一次（避免太频繁）
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if step % 10 == 0:
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self.stream.play_audio(audio_out)
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step += 1
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time.sleep(interval)
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except KeyboardInterrupt:
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print("\n用户中断")
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finally:
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self.running = False
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self.stream.stop()
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print(f"循环结束，共 {step} 步")
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