Anthropic 推出全新硬件标准让 AI 智能体控制物理世界

+-------------------------------------------------------------+
|                       LLM 智能体                             |
|             (例如通过 n1n.ai 调用的 Claude 3.5)              |
+-------------------------------------------------------------+
                               |  (JSON-RPC / MCP 工具调用)
                               v
+-------------------------------------------------------------+
|                   AI 硬件驱动程序接口                        |
|    - Schema 验证      - 安全防护栏      - 状态机管理         |
+-------------------------------------------------------------+
                               |  (标准化低级 API)
                               v
+-------------------------------------------------------------+
|                     设备专用驱动程序                         |
|             (转换为 ROS2, MQTT, Modbus 等信号)               |
+-------------------------------------------------------------+
                               |  (物理电信号)
                               v
+-------------------------------------------------------------+
|                         物理硬件                            |
+-------------------------------------------------------------+

gripper_tool_schema = {
    "name": "control_robotic_gripper",
    "description": "控制物理机械抓手进行抓取、释放或移动物体。安全约束在本地强制执行。",
    "input_schema": {
        "type": "object",
        "properties": {
            "action": {
                "type": "string",
                "enum": ["open", "close", "move"],
                "description": "要执行的物理动作。"
            },
            "grip_force": {
                "type": "number",
                "description": "关闭时的抓紧力（牛顿）。必须在 0.0 到 50.0 之间。"
            },
            "position_z": {
                "type": "integer",
                "description": "距离基板的垂直高度（毫米）。范围：0 到 300。"
            }
        },
        "required": ["action"]
    }
}

import requests
import json
import time

# 模拟物理机械抓手的本地状态
class PhysicalGripper:
    def __init__(self):
        self.is_open = True
        self.current_z = 100 # 单位：毫米 (mm)
        self.max_force = 50.0 # 最大抓紧力：50牛顿 (N)
        self.max_z = 300 # 最大高度：300毫米 (mm)

    def execute(self, action, grip_force=0.0, position_z=None):
        # 本地安全防护栏验证
        if grip_force > self.max_force:
            return {"status": "error", "message": f"安全违规：请求的抓力 {grip_force}N 超过了最大限制 {self.max_force}N。"}
        
        if position_z is not None and (position_z < 0 or position_z > self.max_z):
            return {"status": "error", "message": f"安全违规：位置 {position_z}mm 超出物理范围 (0-{self.max_z}mm)。"}
        
        # 模拟物理动作执行
        if action == "open":
            self.is_open = True
            time.sleep(0.5) # 模拟物理动作的过渡延迟
            return {"status": "success", "message": "机械抓手已成功打开。"}
        elif action == "close":
            self.is_open = False
            time.sleep(0.5)
            return {"status": "success", "message": f"机械抓手已关闭，施加力为 {grip_force}N。"}
        elif action == "move":
            if position_z is not None:
                self.current_z = position_z
                time.sleep(1.0)
                return {"status": "success", "message": f"机械抓手已移动到高度 {position_z}mm。"}
        return {"status": "error", "message": "未知的动作指令。"}

# 初始化硬件
hardware = PhysicalGripper()

# 通过 n1n.ai 平台向 LLM 发送提示词和工具定义
def query_agent(prompt):
    # 将请求路由至 n1n.ai 的统一 API 端点
    url = "https://api.n1n.ai/v1/chat/completions"
    headers = {
        "Authorization": "Bearer YOUR_N1N_API_KEY",
        "Content-Type": "application/json"
    }
    
    payload = {
        "model": "claude-3-5-sonnet",
        "messages": [
            {"role": "user", "content": prompt}
        ],
        "tools": [gripper_tool_schema],
        "tool_choice": "auto"
    }
    
    response = requests.post(url, headers=headers, json=payload)
    return response.json()

# 示例运行
user_instruction = "请帮我拿起那个脆弱的玻璃试管。它位于高度 50mm 处。请注意不要用力过猛。"
print(f"用户指令: {user_instruction}")

# 步骤 1：智能体决定移动并执行抓取
response_data = query_agent(user_instruction)

# 解析工具调用
tool_calls = response_data['choices'][0]['message'].get('tool_calls', [])
if tool_calls:
    for tool_call in tool_calls:
        args = json.loads(tool_call['function']['arguments'])
        print(f"智能体生成的工具调用: {tool_call['function']['name']}，参数: {args}")
        
        # 在物理硬件上执行动作
        result = hardware.execute(
            action=args.get('action'), 
            grip_force=args.get('grip_force', 10.0), 
            position_z=args.get('position_z')
        )
        print(f"硬件执行结果: {result}")