第一次完整测例跑完

unitree_deploy/docs/add_robot_endeffector.md

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+# How to Build Your Own End-Effector [Currently dex_1 and dex_3 are available]
+
+### Define your own config for the end-effector (unitree_deploy/robot_devices/endeffector/config.py)
+
+```python
+@EndEffectorConfig.register_subclass("gripper")  # Register your custom end-effector wrapper. Here it uses def __init__(self, config: GripperConfig):
+@dataclass
+class GripperConfig(EndEffectorConfig):
+    motors: dict[str, tuple[int, str]]
+    unit_test: bool = False
+    control_dt: float = 1/200
+    mock: bool = False
+
+    def __post_init__(self):
+        if self.control_dt < 0.002:
+            raise ValueError(f"`control_dt` must > 1/500 (got {self.control_dt})")
+
+# Default arguments should be placed first [parameters that may need to be customized],
+# Non-default arguments should be placed later [fixed or less important parameters].
+```
+
+### Description of methods in your end-effector class (unitree_deploy/robot_devices/endeffector/utils.py)
+
+```python
+# Base class for EndEffector, extend with required methods
+
+class EndEffector(Protocol):
+    def connect(self): ...
+    def disconnect(self): ...
+    def motor_names(self): ...
+
+    def read_current_endeffector_q(self): ...
+    def read_current_endeffector_dq(self): ...
+    def write_endeffector(self): ...
+
+    def endeffector_ik(self): ...
+```
+
+How to call externally?
+Use make_endeffector_motors_buses_from_configs → Construct the UnitreeRobot class based on the config file
+Use make_endeffector_motors_bus → Construct based on endeffector_type (typically for external module loading)
+
+### Implementation of your end-effector class (unitree_deploy/robot_devices/endeffector/.../....py)
+
+```python
+    # These methods need to be implemented and completed
+    def connect(self): ...
+    def disconnect(self): ...
+    def motor_names(self): ...
+    # connect() and disconnect() should handle initialization and homing respectively
+
+    def read_current_endeffector_q(self): ...
+    def read_current_endeffector_dq(self): ...
+    # Outputs should be unified as np.ndarray
+
+    def write_endeffector(self): ...
+    # Write control commands here
+
+    def arm_ik(self): ...
+    # Wrap IK into your own arm class, to be called externally
+
+    # Private/protected properties
+    # (for reading motor names, IDs, etc. These will be used in UnitreeRobot class for dataset encapsulation)
+    @property
+    def motor_names(self) -> list[str]:
+        return list(self.motors.keys())
+
+    @property
+    def motor_models(self) -> list[str]:
+        return [model for _, model in self.motors.values()]
+
+    @property
+    def motor_indices(self) -> list[int]:
+        return [idx for idx, _ in self.motors.values()]
+```
+
+For arms, use threading to implement \_subscribe_gripper_motor_state (thread for reading motor states),\_ctrl_gripper_motor (thread for motor control),Both threads should run internally within the class.