forked from tangger/lerobot
语音播报
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@@ -260,52 +260,6 @@ class RealmanDualRobot:
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if not self.inference_time:
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self.teleop.reset()
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# def teleop_step(self, record_data=False) -> None | tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]:
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# """遥操作步骤 - 保持原有数据记录逻辑，添加LLM交互"""
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# if not self.is_connected:
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# raise ConnectionError()
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# if self.teleop is None and self.inference_time:
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# self.teleop = HybridController(self.init_info)
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# try:
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# # 检查退出条件
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# if self.q_pressed:
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# print("检测到Q键，任务终止...")
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# speak_async("任务已终止")
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# raise KeyboardInterrupt("用户请求退出")
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# # 执行基础遥操作
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# state = self._read_robot_state()
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# action = self.teleop.get_action(state)
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# self._execute_action(action, state)
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# # 更新状态队列
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# self._update_state_queue()
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# time.sleep(0.019) # 50Hz
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# # 处理数据记录
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# if record_data:
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# data = self._prepare_record_data()
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# if data[0]: # 如果有有效数据
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# # 处理LLM交互请求
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# if self.w_pressed:
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# self.w_pressed = False # 重置标志位
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# self._llm_triggered = True
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# success = self._handle_llm_interaction(data[0])
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# if not success:
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# print("LLM交互处理失败")
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# return data
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# return None
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# except KeyboardInterrupt:
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# # 重新抛出键盘中断，让上层处理
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# raise
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# except Exception as e:
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# logging.error(f"遥操作步骤失败: {e}")
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# return None
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def teleop_step(self, record_data=False) -> None | tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]:
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"""遥操作步骤 - 保持原有数据记录逻辑，添加LLM交互"""
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if not self.is_connected:
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@@ -1,162 +1,3 @@
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# # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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# #
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# # Licensed under the Apache License, Version 2.0 (the "License");
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# # you may not use this file except in compliance with the License.
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# # You may obtain a copy of the License at
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# #
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# # http://www.apache.org/licenses/LICENSE-2.0
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# #
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# # Unless required by applicable law or agreed to in writing, software
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# # distributed under the License is distributed on an "AS IS" BASIS,
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# # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# # See the License for the specific language governing permissions and
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# # limitations under the License.
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# conversation_history = []
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# conversation_client = None
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# from typing import Protocol, Dict
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# # Robot configuration imports
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# from lerobot.common.robot_devices.robots.configs import (
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# AlohaRobotConfig,
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# KochBimanualRobotConfig,
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# KochRobotConfig,
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# LeKiwiRobotConfig,
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# ManipulatorRobotConfig,
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# MossRobotConfig,
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# RobotConfig,
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# So100RobotConfig,
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# So101RobotConfig,
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# StretchRobotConfig,
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# RealmanRobotConfig,
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# RealmanDualRobotConfig
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# )
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# # Added library imports for LLM interaction
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# from openai import OpenAI
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# import base64
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# import os
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# import cv2
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# import torch
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# import numpy as np
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# from pynput import keyboard
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# import time
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# import json
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# from datetime import datetime
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# def get_arm_id(name, arm_type):
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# """Returns the string identifier of a robot arm."""
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# return f"{name}_{arm_type}"
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# class Robot(Protocol):
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# robot_type: str
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# features: dict
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# cameras: Dict
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# def connect(self): ...
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# def run_calibration(self): ...
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# def teleop_step(self, record_data=False): ...
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# def capture_observation(self) -> Dict: ...
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# def send_action(self, action): ...
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# def disconnect(self): ...
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# def make_robot_config(robot_type: str, **kwargs) -> RobotConfig:
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# # ... (此函数内容保持不变) ...
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# if robot_type == "aloha":
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# return AlohaRobotConfig(**kwargs)
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# elif robot_type == "koch":
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# return KochRobotConfig(**kwargs)
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# elif robot_type == "koch_bimanual":
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# return KochBimanualRobotConfig(**kwargs)
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# elif robot_type == "moss":
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# return MossRobotConfig(**kwargs)
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# elif robot_type == "so100":
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# return So100RobotConfig(**kwargs)
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# elif robot_type == "so101":
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# return So101RobotConfig(**kwargs)
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# elif robot_type == "stretch":
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# return StretchRobotConfig(**kwargs)
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# elif robot_type == "lekiwi":
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# return LeKiwiRobotConfig(**kwargs)
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# elif robot_type == 'realman':
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# return RealmanRobotConfig(**kwargs)
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# elif robot_type == 'realman_dual':
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# return RealmanDualRobotConfig(**kwargs)
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# else:
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# raise ValueError(f"Robot type '{robot_type}' is not available.")
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# def make_robot_from_config(config: RobotConfig):
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# # ... (此函数内容保持不变) ...
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# if isinstance(config, ManipulatorRobotConfig):
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# from lerobot.common.robot_devices.robots.manipulator import ManipulatorRobot
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# return ManipulatorRobot(config)
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# elif isinstance(config, LeKiwiRobotConfig):
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# from lerobot.common.robot_devices.robots.mobile_manipulator import MobileManipulator
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# return MobileManipulator(config)
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# elif isinstance(config, RealmanRobotConfig):
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# from lerobot.common.robot_devices.robots.realman import RealmanRobot
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# return RealmanRobot(config)
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# elif isinstance(config, RealmanDualRobotConfig):
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# from lerobot.common.robot_devices.robots.realman_dual import RealmanDualRobot
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# return RealmanDualRobot(config)
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# else:
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# from lerobot.common.robot_devices.robots.stretch import StretchRobot
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# return StretchRobot(config)
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# def make_robot(robot_type: str, **kwargs) -> Robot:
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# config = make_robot_config(robot_type, **kwargs)
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# return make_robot_from_config(config)
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# # -------------------- LLM 交互功能区 --------------------
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# def encode_image_to_base64(image_tensor: torch.Tensor) -> str:
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# """将PyTorch张量格式的图像编码为Base64字符串"""
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# image_np = image_tensor.cpu().numpy().astype(np.uint8)
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# image_bgr = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
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# success, buffer = cv2.imencode(".jpg", image_bgr)
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# if not success:
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# raise ValueError("图像编码失败")
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# return base64.b64encode(buffer).decode("utf-8")
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# # 在文件顶部添加全局变量来管理会话
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# conversation_history = []
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# conversation_client = None
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# def ask_llm(query: str, state: dict):
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# prompt = """ """
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# api_key = os.getenv("OPENAI_API_KEY")
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# base_url = os.getenv("OPENAI_BASE_URL")
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# client = OpenAI(api_key=api_key, base_url=base_url)
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# # keys = [key for key in state]
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# # import pdb
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# # pdb.set_trace()
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# pass
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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