实时将大模型的解决方案转换为随机应变的机器人指令
1. 自然语言到机器人动作的实时转换技术
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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_commandsdef _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
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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 responsedef _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
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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 commandsasync 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
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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) orsensor_data.get('complex_situation', False) orsensor_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：
自然语言理解：解析大模型输出的自由文本
动作语义解析：将"绕过去"、"跨过去"等语义转换为具体动作
指令序列生成：生成时间有序的机器人控制指令
安全约束集成：确保生成的动作在物理限制内
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
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# 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