1. 自然语言到机器人动作的实时转换技术

python

class DynamicActionTranslator:
    def __init__(self):
        self.primitive_actions = {
            # 基础动作库
            "move": self._parse_move_action,
            "grasp": self._parse_grasp_action, 
            "step": self._parse_step_action,
            "avoid": self._parse_avoid_action,
            "lift": self._parse_lift_action,
            "push": self._parse_push_action
        }
    
    async def translate_llm_solution(self, llm_natural_language: str) -> List[RobotCommand]:
        """将大模型的自然语言解决方案实时转换为机器人指令"""
        
        # 1. 动作提取和解析
        action_sequence = self._extract_actions(llm_natural_language)
        
        # 2. 生成具体指令序列
        robot_commands = []
        for action in action_sequence:
            if action["type"] in self.primitive_actions:
                commands = await self.primitive_actions[action["type"]](action)
                robot_commands.extend(commands)
        
        return robot_commands
    
    def _extract_actions(self, natural_language: str) -> List[dict]:
        """从自然语言中提取结构化动作序列"""
        # 使用大模型进行动作解析（可以调用同一个或专门的解析模型）
        prompt = f"""
        将以下机器人操作描述解析为结构化动作序列：
        「{natural_language}」
        
        输出格式：
        [
          {{"type": "move", "parameters": {{"direction": "forward", "distance": 1.0}}}},
          {{"type": "grasp", "parameters": {{"object": "box", "grip_force": 0.5}}}}
        ]
        """
        
        # 调用大模型进行解析
        structured_output = self._call_llm_for_parsing(prompt)
        return self._validate_actions(structured_output)

2. 实时解决方案生成流程

python

class RealTimeSolutionExecutor:
    def __init__(self):
        self.translator = DynamicActionTranslator()
        self.llm_client = LLMClient()
        self.robot_controller = RobotController()
    
    async def handle_unknown_situation(self, sensor_data: dict) -> None:
        """处理未知情况的完整流程"""
        
        # 1. 大模型实时分析并生成解决方案
        llm_solution = await self._get_llm_solution(sensor_data)
        print(f"大模型解决方案: {llm_solution}")
        
        # 2. 实时转换为机器人指令
        robot_commands = await self.translator.translate_llm_solution(llm_solution)
        
        # 3. 安全验证和执行
        await self._execute_with_safety_check(robot_commands)
    
    async def _get_llm_solution(self, sensor_data: dict) -> str:
        """让大模型根据当前情况生成解决方案"""
        
        prompt = self._build_situation_prompt(sensor_data)
        response = await self.llm_client.generate(prompt)
        return response
    
    def _build_situation_prompt(self, sensor_data: dict) -> str:
        """构建给大模型的场景描述提示词"""
        
        return f"""
        你是一个机器人控制专家。机器人遇到了以下情况：
        
        环境信息：
        - 前方障碍物类型：{sensor_data['obstacle_type']}
        - 障碍物尺寸：{sensor_data['obstacle_size']}
        - 机器人能力：移动、抓取、跨越、避障
        - 目标位置：前方{sensor_data['target_distance']}米处
        
        请给出具体的操作步骤，让机器人能够应对这个情况。描述要具体，比如：
        "先向左移动0.5米绕开障碍物，然后向前移动2米"
        "用机械手抓住箱子并向右侧移动1米"
        "抬起前腿跨过障碍物，步高约15厘米"
        
        当前情况：{sensor_data['situation_description']}
        
        你的解决方案：
        """

3. 具体动作解析器实现

python

class ActionParser:
    async def _parse_move_action(self, action: dict) -> List[RobotCommand]:
        """解析移动类动作"""
        params = action["parameters"]
        
        commands = []
        if "direction" in params:
            # 解析方向移动
            if params["direction"] == "left":
                commands.append(RobotCommand("rotate", {"angle": 90}))
            elif params["direction"] == "right":
                commands.append(RobotCommand("rotate", {"angle": -90}))
            
            # 解析距离
            distance = params.get("distance", 1.0)
            commands.append(RobotCommand("move_forward", {"distance": distance}))
        
        return commands
    
    async def _parse_grasp_action(self, action: dict) -> List[RobotCommand]:
        """解析抓取动作"""
        params = action["parameters"]
        
        return [
            RobotCommand("approach_object", {"object_type": params["object"]}),
            RobotCommand("open_gripper", {}),
            RobotCommand("move_to_grasp_position", {}),
            RobotCommand("close_gripper", {"force": params.get("grip_force", 0.5)})
        ]
    
    async def _parse_step_action(self, action: dict) -> List[RobotCommand]:
        """解析跨越动作"""
        params = action["parameters"]
        
        return [
            RobotCommand("lift_leg", {"height": params.get("step_height", 0.2)}),
            RobotCommand("move_leg_forward", {"distance": params.get("step_length", 0.6)}),
            RobotCommand("lower_leg", {}),
            RobotCommand("shift_weight", {})
        ]

4. 完整实时应变系统

python

class AdaptiveRobotSystem:
    def __init__(self):
        self.executor = RealTimeSolutionExecutor()
        self.sensor_processor = SensorProcessor()
    
    async def run_adaptive_navigation(self):
        """运行自适应导航"""
        while True:
            # 1. 感知环境
            sensor_data = await self.sensor_processor.get_latest_data()
            
            # 2. 检测是否需要特殊处理
            if self._needs_special_handling(sensor_data):
                # 3. 实时调用大模型生成解决方案并执行
                await self.executor.handle_unknown_situation(sensor_data)
            else:
                # 正常导航
                await self._normal_navigation()
            
            await asyncio.sleep(0.1)  # 控制循环频率
    
    def _needs_special_handling(self, sensor_data: dict) -> bool:
        """判断是否需要大模型介入处理"""
        return (
            sensor_data.get('unexpected_obstacle', False) or
            sensor_data.get('complex_situation', False) or
            sensor_data.get('navigation_failed', False)
        )

# 使用示例
async def main():
    robot = AdaptiveRobotSystem()
    
    # 模拟遇到未知障碍物
    sensor_data = {
        'obstacle_type': '可移动的纸箱',
        'obstacle_size': '30x30x40cm',
        'target_distance': 5.0,
        'situation_description': '前方有一个中等大小的纸箱挡住了去路，纸箱看起来不重',
        'unexpected_obstacle': True
    }
    
    await robot.executor.handle_unknown_situation(sensor_data)

5. 技术核心要点

这种实时应变技术的核心是：

1. 自然语言理解：解析大模型输出的自由文本

2. 动作语义解析：将"绕过去"、"跨过去"等语义转换为具体动作

3. 指令序列生成：生成时间有序的机器人控制指令

4. 安全约束集成：确保生成的动作在物理限制内

5. 实时执行监控：在执行过程中持续监控和调整

6. 实际应用场景

python

# 大模型可能输出的解决方案示例
llm_solutions = [
    "先向右移动0.8米绕过树木，然后继续向前走3米",
    "用机械手轻轻推开箱子到左侧，注意保持平衡",
    "抬起前腿跨过这个矮栏杆，步高约20厘米",
    "慢慢后退1米，然后向左转45度从侧面通过"
]

# 这些都会被实时转换为具体的机器人指令序列