第一次完整测例跑完

unitree_deploy/docs/GettingStarted.md

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+# Getting Started
+
+### Code framework
+
+
+| Module Name              | Documentation Link                                 |
+| ------------------------- | -------------------------------------------------- |
+| robots                    | [build_robot](./build_robot.md)                    |
+| robot_devices/arm         | [add_robot_arm](./add_robot_arm.md)                |
+| robot_devices/cameras     | [add_robot_camera](./add_robot_camera.md)          |
+| robot_devices/endeffector | [add_robot_endeffector](./add_robot_endeffector.md)|
+
+### Simple Usage (Example code, not executable)
+
+```python
+import time
+import math
+import torch
+
+from unitree_deploy.robot.robot_utils import make_robot
+from unitree_deploy.robot_devices.robots_devices_utils import precise_wait
+
+class YourPolicy:
+    def predict_action(self, observation, policy):
+        # Logic for predicting action
+        pass
+
+class UnitreeEnv:
+    def __init__(self):
+        self.robot = make_robot(self.robot_type)
+        if not self.robot.is_connected:
+            self.robot.connect()
+            # If disconnection is needed, call disconnect() here
+            # self.robot.disconnect()
+
+    def get_obs(self):
+        # Get observation
+        observation = self.robot.capture_observation()
+        return observation
+
+    def step(self, pred_action, t_command_target):
+        # Execute action
+        t_cycle_end = time.monotonic() + self.control_dt
+        t_command_target = t_cycle_end + self.control_dt
+        action = self.robot.send_action(torch.from_numpy(pred_action), t_command_target)
+        precise_wait(t_cycle_end)
+        return action
+
+if __name__ == "__main__":
+    policy = YourPolicy()  # Create policy instance
+    env = UnitreeEnv()     # Create environment instance
+
+    t_start = time.monotonic()   # Get start time
+    iter_idx = 0                 # Initialize iteration index
+    control_dt = 1 / 30          # Control loop interval (30Hz)
+
+    try:
+        while True:
+            t_cycle_end = t_start + (iter_idx + 1) * control_dt   # Calculate end time of current cycle
+            t_command_target = t_cycle_end + control_dt           # Calculate command target time
+
+            observation = env.get_obs()                          # Get environment observation
+            pred_action = policy.predict_action(observation, policy)  # Predict action
+            env.step(pred_action, t_command_target)               # Execute action
+
+            precise_wait(t_cycle_end)                             # Wait until cycle end
+            iter_idx += 1                                         # Update iteration index
+    finally:
+        # Perform cleanup operations on exit (e.g., disconnect robot)
+        pass