清理多余策略

Deploy_Tienkung/FSM/fsm_base.py

@@ -14,8 +14,6 @@ class FSMStateName(Enum):
     STOP = 0      # 停止状态
     ZERO = 1      # 零位状态
     WALKAMP = 2   # WALKAMP策略状态
-    MYPOLICY = 3  # 自定义策略状态
-    XSIMRUN = 4   # 更贴近sim2sim的xSIM run状态
 
 class FSMState(ABC):
     """FSM状态抽象基类"""

Deploy_Tienkung/FSM/robot_fsm.py

@@ -5,8 +5,6 @@ Complete FSM implementation with state management
 from typing import Dict
 from .fsm_base import RobotFSM, FSMStateName
 from policy.walk_amp.fsm_walkamp import FSMStateWALKAMP
-from policy.mypolicy.fsm_mypolicy import FSMStateMYPOLICY
-from policy.xsim_run.fsm_xsim_run import FSMStateXSIMRUN
 from policy.zero.fsm_zero import FSMStateZero
 from policy.stop.fsm_stop import FSMStateStop
 from policy.beyond_mimic.fsm_beyond_mimic import FSMStateBeyondMimic
@@ -52,8 +50,6 @@ class RobotFSMImpl(RobotFSM):
         self.state_objects[FSMStateName.STOP] = FSMStateStop(self.robot_data_)
         self.state_objects[FSMStateName.ZERO] = FSMStateZero(self.robot_data_)
         self.state_objects[FSMStateName.WALKAMP] = FSMStateWALKAMP(self.robot_data_)
-        self.state_objects[FSMStateName.MYPOLICY] = FSMStateMYPOLICY(self.robot_data_)
-        self.state_objects[FSMStateName.XSIMRUN] = FSMStateXSIMRUN(self.robot_data_)
 
         # TODO: 添加其他状态对象
     @timing_decorator

Deploy_Tienkung/common/robot_interface.py

@@ -194,8 +194,6 @@ class RobotInterfaceImpl(RobotInterface):
             "STOP": FSMStateName.STOP,
             "ZERO": FSMStateName.ZERO,
             "WALKAMP": FSMStateName.WALKAMP,
-            "MYPOLICY": FSMStateName.MYPOLICY,
-            "XSIMRUN": FSMStateName.XSIMRUN,
         }
         self.waist_control_status = [state_to_FSMState[state] for state in config.get('waist_control_status')]
         self.legs_control_status = [state_to_FSMState[state] for state in config.get('legs_control_status')]

Deploy_Tienkung/common/stdin_keyboard_control.py

@@ -61,8 +61,6 @@ class KeyboardController:
         print("  z - Goto ZERO state")
         print("  c - Goto STOP state")
         print("  m - Goto WALKAMP state")
-        print("  p - Goto MYPOLICY state")
-        print("  n - Goto XSIMRUN state")
         print("  Left/Right arrows - Adjust height")
         print("  w/a/s/d - Movement controls")
         print("  q/e - Rotation controls (turn left/right)")
@@ -196,10 +194,6 @@ class KeyboardController:
             self._on_x_key()
         elif key == '4':
             self._on_g_key()
-        elif key == 'p':
-            self._on_p_key()
-        elif key == 'n':
-            self._on_n_key()
         elif key == '6':
             self._on_o_key()
         elif key == 'v':
@@ -359,26 +353,6 @@ class KeyboardController:
             self.last_fsm_command_time = time.time()
             print("Command: gotoWALKAMP")
     
-    def _on_p_key(self):
-        """处理p键 - 切换到MYPOLICY状态"""
-        with self.data_mutex:
-            self.keyboard_flag.x_speed_command = 0.0
-            self.keyboard_flag.y_speed_command = 0.0
-            self.keyboard_flag.yaw_speed_command = 0.0
-            self.keyboard_flag.fsm_state_command = "gotoMYPOLICY"
-            self.last_fsm_command_time = time.time()
-            print("Command: gotoMYPOLICY (movement commands reset to zero)")
-
-    def _on_n_key(self):
-        """处理n键 - 切换到XSIMRUN状态"""
-        with self.data_mutex:
-            self.keyboard_flag.x_speed_command = 0.0
-            self.keyboard_flag.y_speed_command = 0.0
-            self.keyboard_flag.yaw_speed_command = 0.0
-            self.keyboard_flag.fsm_state_command = "gotoXSIMRUN"
-            self.last_fsm_command_time = time.time()
-            print("Command: gotoXSIMRUN (movement commands reset to zero)")
-    
     def _handle_ctrl_c(self):
         """处理Ctrl+C - 发送SIGINT信号给主进程"""
         # 先停止键盘控制器

Deploy_Tienkung/config/dex_config.yaml

@@ -42,9 +42,9 @@ xbox:
 
 robot_interface:
   clip_actions: false
-  waist_control_status: ["ZERO", "STOP", "WALKAMP", "MYPOLICY", "XSIMRUN"] #
+  waist_control_status: ["ZERO", "STOP", "WALKAMP"] #
   legs_control_status: [] #空代表都允许控制，仅腿部是这个逻辑
-  arms_control_status: ["ZERO", "STOP", "WALKAMP", "MYPOLICY", "XSIMRUN"] #
+  arms_control_status: ["ZERO", "STOP", "WALKAMP"] #
   left_arm_only_status: []
   right_arm_only_status: []
   xsense_data_roll_offset: 0.0

Deploy_Tienkung/policy/mypolicy/config/mypolicy.yaml

@@ -1,96 +0,0 @@
-model_path: "policy.onnx"
-motor_num: 29
-actions_size: 23
-dt: 0.01
-warm_start_time: 0.0
-xsense_data_roll_offset: 0.0
-joint_names: [
-  hip_pitch_l_joint, hip_pitch_r_joint, waist_yaw_joint,
-  hip_roll_l_joint, hip_roll_r_joint, waist_roll_joint,
-  hip_yaw_l_joint, hip_yaw_r_joint, waist_pitch_joint,
-  knee_pitch_l_joint, knee_pitch_r_joint,
-  shoulder_pitch_l_joint, shoulder_pitch_r_joint,
-  ankle_pitch_l_joint, ankle_pitch_r_joint,
-  shoulder_roll_l_joint, shoulder_roll_r_joint,
-  ankle_roll_l_joint, ankle_roll_r_joint,
-  shoulder_yaw_l_joint, shoulder_yaw_r_joint,
-  elbow_pitch_l_joint, elbow_pitch_r_joint
-]
-zero_pos_offset: [
-      0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-      0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-      0.0, 0.0, 0.0,
-      0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-      0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
-    ]
-ct_scale: [
-          2.1, 2.1, 3.207, 2.673, 2.6, 2.6,
-          2.1, 2.1, 3.207, 2.673, 2.6, 2.6,
-          3.207, 3.207, 3.207,
-          3.207, 2.28, 5.89, 5.89, 3.35, 2.3, 2.3,
-          3.207, 2.28, 5.89, 5.89, 3.35, 2.3, 2.3
-    ]
-control:
-  action_scale: 0.25
-  decimation: 2
-
-gait:
-  gait_air_ratio_l: 0.6
-  gait_air_ratio_r: 0.6
-  gait_phase_offset_l: 0.6
-  gait_phase_offset_r: 0.1
-  gait_cycle: 0.64
-
-normalization:
-  clip_scales:
-    clip_observations: 100.0
-    clip_actions: 100.0
-  obs_scales:
-    lin_vel: 1.0
-    ang_vel: 1.0
-    dof_pos: 1.0
-    dof_vel: 1.0
-
-size:
-  num_hist: 10
-  observations_size: 84
-
-gains:
-  kp: [
-    300.0, 300.0, 400.0,
-    300.0, 300.0, 400.0,
-    150.0, 150.0, 400.0,
-    350.0, 350.0,
-    150.0, 150.0,
-    30.0, 30.0,
-    50.0, 50.0,
-    16.8, 16.8,
-    50.0, 50.0,
-    150.0, 150.0
-  ]
-  kd: [
-    10.0, 10.0, 5.0,
-    10.0, 10.0, 10.0,
-    5.0, 5.0, 10.0,
-    10.0, 10.0,
-    7.5, 7.5,
-    2.5, 2.5,
-    2.5, 2.5,
-    1.4, 1.4,
-    2.5, 2.5,
-    5.0, 5.0
-  ]
-
-init_state:
-  default_joint_angles: [
-    0.0, 0.0, 0.0,
-    -0.5, -0.5, 0.0,
-    0.0, 0.0, 0.0,
-    1.0, 1.0,
-    0.0, 0.0,
-    -0.5, -0.5,
-    0.2, -0.2,
-    0.0, 0.0,
-    0.0, 0.0,
-    -0.3, -0.3
-  ]

Deploy_Tienkung/policy/mypolicy/fsm_mypolicy.py

@@ -1,373 +0,0 @@
-"""
-FSM state implementation for the standalone MYPOLICY controller.
-"""
-
-import os
-
-import numpy as np
-import onnxruntime as ort
-import yaml
-from scipy.spatial.transform import Rotation
-
-from FSM.fsm_base import FSMState, FSMStateName
-from common.BasicFunction import gait_phase
-from common.joystick import ControlFlag
-from common.robot_data import RobotData
-
-
-class FSMStateMYPOLICY(FSMState):
-    """Standalone FSM implementation for the custom ONNX policy."""
-
-    def __init__(self, robot_data: RobotData):
-        super().__init__(robot_data)
-        self.current_state_name = FSMStateName.MYPOLICY
-        self.log_prefix = "FSMStateMYPOLICY"
-
-        current_dir = os.path.dirname(os.path.abspath(__file__))
-        config_path = os.path.join(current_dir, "config", "mypolicy.yaml")
-        with open(config_path, "r") as f:
-            policy_config = yaml.safe_load(f)
-
-        self.action_num_ = policy_config.get("actions_size")
-        self.motor_num_ = policy_config.get("motor_num")
-        self.dt_ = policy_config.get("dt")
-
-        size_config = policy_config.get("size", {})
-        self.num_hist_ = size_config.get("num_hist")
-        self.obs_size_ = size_config.get("observations_size")
-
-        control_config = policy_config.get("control", {})
-        self.action_scale_ = control_config.get("action_scale")
-        self.decimation_ = control_config.get("decimation")
-        self.warm_start_time_ = control_config.get(
-            "warm_start_time",
-            policy_config.get("warm_start_time", 0.3),
-        )
-
-        norm_config = policy_config.get("normalization", {})
-        clip_config = norm_config.get("clip_scales", {})
-        obs_config = norm_config.get("obs_scales", {})
-        self.clip_obs_ = clip_config.get("clip_observations", 100.0)
-        self.clip_act_ = clip_config.get("clip_actions", 100.0)
-        self.lin_vel_scale_ = obs_config.get("lin_vel")
-        self.ang_vel_scale_ = obs_config.get("ang_vel")
-        self.dof_pos_scale_ = obs_config.get("dof_pos")
-        self.dof_vel_scale_ = obs_config.get("dof_vel")
-
-        self.observations_ = np.zeros(self.obs_size_ * self.num_hist_, dtype=np.float32)
-        self.proprio_hist_buf_ = np.zeros(self.obs_size_ * self.num_hist_, dtype=np.float32)
-        self.last_actions_ = np.zeros(self.action_num_, dtype=np.float32)
-        self.actions_ = np.zeros(self.action_num_, dtype=np.float32)
-        self._warm_start_pose = np.zeros(self.motor_num_, dtype=np.float32)
-
-        self.is_first_obs_ = True
-        self.is_first_action_ = True
-        self.is_first_step_ = True
-        self.timer_gait_ = 0.0
-
-        gait_config = policy_config.get("gait", {})
-        self.gait_cycle = gait_config.get("gait_cycle", 0.64)
-        self.left_phase_ratio = gait_config.get("gait_air_ratio_l", 0.6)
-        self.right_phase_ratio = gait_config.get("gait_air_ratio_r", 0.6)
-        self.left_theta_offset = gait_config.get("gait_phase_offset_l", 0.6)
-        self.right_theta_offset = gait_config.get("gait_phase_offset_r", 0.1)
-
-        step = (self.decimation_ if self.decimation_ else 1) * self.dt_
-        if self.warm_start_time_ > 0 and step > 0:
-            self._warm_start_steps = max(1, int(self.warm_start_time_ / step))
-        else:
-            self._warm_start_steps = 0
-        self._warmup_inference_counter = 0
-
-        self.waiting_for_motion = True
-        self.motion_threshold = 1e-3
-        self.hold_pose = np.zeros(self.motor_num_, dtype=np.float32)
-        self.filtered_x_speed = 0.0
-
-        self.model_path = os.path.join(current_dir, "model", policy_config["model_path"])
-        self._init_onnx_session()
-
-        joint_names = policy_config.get("joint_names")
-        if joint_names is None:
-            raise ValueError("[FSMStateMYPOLICY] Missing 'joint_names' in mypolicy.yaml")
-        self.joint_seq = list(joint_names)
-
-        if self.action_scale_ is None:
-            raise ValueError("[FSMStateMYPOLICY] Missing 'control.action_scale' in mypolicy.yaml")
-        if np.isscalar(self.action_scale_):
-            self.action_scale = np.full(len(self.joint_seq), float(self.action_scale_), dtype=np.float32)
-        else:
-            self.action_scale = np.array(self.action_scale_, dtype=np.float32)
-        if len(self.action_scale) != len(self.joint_seq):
-            raise ValueError(
-                f"[FSMStateMYPOLICY] control.action_scale length {len(self.action_scale)} does not match joint count {len(self.joint_seq)}"
-            )
-
-        init_state_config = policy_config.get("init_state", {})
-        default_joint_angles = init_state_config.get("default_joint_angles")
-        if default_joint_angles is None:
-            raise ValueError("[FSMStateMYPOLICY] Missing 'init_state.default_joint_angles' in mypolicy.yaml")
-        self.joint_pos_array_seq = np.array(default_joint_angles, dtype=np.float32)
-        if len(self.joint_pos_array_seq) != len(self.joint_seq):
-            raise ValueError(
-                f"[FSMStateMYPOLICY] init_state.default_joint_angles length {len(self.joint_pos_array_seq)} does not match joint count {len(self.joint_seq)}"
-            )
-
-        gains_config = policy_config.get("gains", {})
-        kp_values = gains_config.get("kp")
-        kd_values = gains_config.get("kd")
-        if kp_values is None or kd_values is None:
-            raise ValueError("[FSMStateMYPOLICY] Missing 'gains.kp' or 'gains.kd' in mypolicy.yaml")
-        self.stiffness_array_seq = np.array(kp_values, dtype=np.float32)
-        self.damping_array_seq = np.array(kd_values, dtype=np.float32)
-        if len(self.stiffness_array_seq) != len(self.joint_seq):
-            raise ValueError(
-                f"[FSMStateMYPOLICY] gains.kp length {len(self.stiffness_array_seq)} does not match joint count {len(self.joint_seq)}"
-            )
-        if len(self.damping_array_seq) != len(self.joint_seq):
-            raise ValueError(
-                f"[FSMStateMYPOLICY] gains.kd length {len(self.damping_array_seq)} does not match joint count {len(self.joint_seq)}"
-            )
-
-        self.joint_xml = [
-            "hip_pitch_l_joint", "hip_roll_l_joint", "hip_yaw_l_joint",
-            "knee_pitch_l_joint", "ankle_pitch_l_joint", "ankle_roll_l_joint",
-            "hip_pitch_r_joint", "hip_roll_r_joint", "hip_yaw_r_joint",
-            "knee_pitch_r_joint", "ankle_pitch_r_joint", "ankle_roll_r_joint",
-            "waist_yaw_joint", "waist_roll_joint", "waist_pitch_joint",
-            "shoulder_pitch_l_joint", "shoulder_roll_l_joint", "shoulder_yaw_l_joint",
-            "elbow_pitch_l_joint", "elbow_yaw_l_joint", "wrist_pitch_l_joint", "wrist_roll_l_joint",
-            "shoulder_pitch_r_joint", "shoulder_roll_r_joint", "shoulder_yaw_r_joint",
-            "elbow_pitch_r_joint", "elbow_yaw_r_joint", "wrist_pitch_r_joint", "wrist_roll_r_joint",
-        ]
-
-        self.lab2mj = []
-        for name in self.joint_seq:
-            if name not in self.joint_xml:
-                raise ValueError(f"[FSMStateMYPOLICY] joint '{name}' from mypolicy.yaml not found in joint_xml")
-            self.lab2mj.append(self.joint_xml.index(name))
-        self.lab2mj = np.array(self.lab2mj, dtype=int)
-
-        n_mj = len(self.joint_xml)
-        self.joint_pos_array = np.zeros(n_mj, dtype=np.float32)
-        self.stiffness_array = np.zeros(n_mj, dtype=np.float32)
-        self.damping_array = np.zeros(n_mj, dtype=np.float32)
-        for lab_idx, mj_idx in enumerate(self.lab2mj):
-            self.joint_pos_array[mj_idx] = self.joint_pos_array_seq[lab_idx]
-            self.stiffness_array[mj_idx] = self.stiffness_array_seq[lab_idx]
-            self.damping_array[mj_idx] = self.damping_array_seq[lab_idx]
-
-        self.kps_lab = self.stiffness_array_seq
-        self.kds_lab = self.damping_array_seq
-        self.default_angles_lab = self.joint_pos_array_seq
-        self.action_scale_lab = self.action_scale
-
-    def _init_onnx_session(self):
-        try:
-            options = ort.SessionOptions()
-            options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
-            options.intra_op_num_threads = 1
-            options.inter_op_num_threads = 1
-            options.enable_mem_pattern = False
-            options.enable_mem_reuse = True
-            self.ort_session_ = ort.InferenceSession(
-                self.model_path,
-                options,
-                providers=["CPUExecutionProvider"],
-            )
-            print(f"[{self.log_prefix}-ONNX] ONNX model loaded successfully: {self.model_path}")
-        except Exception as e:
-            print(f"[{self.log_prefix}] Failed to load ONNX model: {e}")
-            self.ort_session_ = None
-
-    def _reset_internal_state(self):
-        self.observations_.fill(0.0)
-        self.proprio_hist_buf_.fill(0.0)
-        self.last_actions_.fill(0.0)
-        self.actions_.fill(0.0)
-        self.is_first_obs_ = True
-        self.is_first_action_ = True
-        self.is_first_step_ = True
-
-        base = self.robot_data_.q_d_.shape[0] - self.motor_num_
-        self.robot_data_.q_d_[base:base + len(self.joint_xml)] = self.joint_pos_array
-        self.robot_data_.q_dot_d_[base:base + len(self.joint_xml)] = 0.0
-        self.robot_data_.tau_d_[base:base + len(self.joint_xml)] = 0.0
-
-    def on_enter(self):
-        self._reset_internal_state()
-        print(f"[{self.log_prefix}] enter")
-        self.timer_gait_ = 0.0
-        self.waiting_for_motion = True
-        self._warmup_inference_counter = 0
-        if self.robot_data_ is not None:
-            try:
-                current_pose = self.robot_data_.get_joint_pos().copy()
-                self._warm_start_pose = current_pose
-                self.hold_pose = current_pose
-            except Exception:
-                self._warm_start_pose.fill(0.0)
-                self.hold_pose.fill(0.0)
-        else:
-            self._warm_start_pose.fill(0.0)
-            self.hold_pose.fill(0.0)
-        print(f"[{self.log_prefix}] waiting for motion command before starting policy")
-
-    def run(self, flag: ControlFlag):
-        walk_cmd = np.array(self.robot_data_.get_walk_cmd(), dtype=np.float32)
-        if self.waiting_for_motion:
-            if np.max(np.abs(walk_cmd)) <= self.motion_threshold:
-                base = self.robot_data_.q_d_.shape[0] - self.motor_num_
-                self.robot_data_.q_d_[base:base + len(self.joint_xml)] = self.hold_pose
-                self.robot_data_.q_dot_d_[base:base + len(self.joint_xml)] = 0.0
-                self.robot_data_.tau_d_[base:base + len(self.joint_xml)] = 0.0
-                self.robot_data_.joint_kp_p_[:len(self.joint_xml)] = self.stiffness_array
-                self.robot_data_.joint_kd_p_[:len(self.joint_xml)] = self.damping_array
-                return
-            self.waiting_for_motion = False
-            self._warm_start_pose = self.robot_data_.get_joint_pos().copy()
-            self._warmup_inference_counter = 0
-            print(f"[{self.log_prefix}] motion command detected: {walk_cmd}, policy activated")
-
-        print(f"[{self.log_prefix}] run")
-        gait = gait_phase(
-            self.timer_gait_,
-            self.gait_cycle,
-            self.left_theta_offset,
-            self.right_theta_offset,
-            self.left_phase_ratio,
-            self.right_phase_ratio,
-        ).astype(np.float32)
-
-        if int(self.robot_data_.time_now_ / self.dt_) % self.decimation_ == 0:
-            self.compute_observation(flag, gait)
-            self.compute_actions()
-
-            target_dof_pos_lab = self.actions_ * self.action_scale_lab + self.default_angles_lab
-            target_dof_pos_mj = self.robot_data_.get_joint_pos().copy()
-            target_dof_pos_mj[self.lab2mj] = target_dof_pos_lab
-            commanded_pos = target_dof_pos_mj
-            if self._warm_start_steps > 0 and self._warmup_inference_counter < self._warm_start_steps:
-                self._warmup_inference_counter += 1
-                blend = self._warmup_inference_counter / float(self._warm_start_steps)
-                commanded_pos = (1.0 - blend) * self._warm_start_pose + blend * target_dof_pos_mj
-
-            base = self.robot_data_.q_d_.shape[0] - self.motor_num_
-            self.robot_data_.q_d_[base:base + len(self.joint_xml)] = commanded_pos
-            self.robot_data_.q_dot_d_[base:base + len(self.joint_xml)] = 0.0
-            self.robot_data_.tau_d_[base:base + len(self.joint_xml)] = 0.0
-            self.last_actions_[:] = self.actions_
-
-        self.timer_gait_ += self.dt_
-        self.robot_data_.joint_kp_p_[:len(self.joint_xml)] = self.stiffness_array
-        self.robot_data_.joint_kd_p_[:len(self.joint_xml)] = self.damping_array
-
-    def compute_observation(self, flag: ControlFlag, gait):
-        roll, pitch, yaw = (
-            float(self.robot_data_.imu_data_[2]),
-            float(self.robot_data_.imu_data_[1]),
-            float(self.robot_data_.imu_data_[0]),
-        )
-        quat_wxyz = self.euler_to_quaternion_scipy(roll, pitch, yaw)
-        q_xyzw = np.array([quat_wxyz[1], quat_wxyz[2], quat_wxyz[3], quat_wxyz[0]], dtype=np.float32)
-        gravity_init = self.quat_rotate_inverse_numpy(q_xyzw, np.array([0.0, 0.0, -1.0], dtype=np.float32))
-
-        x_speed_command, y_speed_command, yaw_speed_command = self.robot_data_.get_walk_cmd()
-        new_filtered_x_speed = x_speed_command
-        change = new_filtered_x_speed - self.filtered_x_speed
-        change = np.clip(change, -0.005, 0.005)
-        self.filtered_x_speed = self.filtered_x_speed + change
-        command = np.array(
-            [x_speed_command, y_speed_command, yaw_speed_command],
-            dtype=np.float32,
-        )
-        print(f"Input command: {command}")
-
-        ang_vel = self.robot_data_.get_angular_velocity()
-        q_mj = self.robot_data_.get_joint_pos()
-        dq_mj = self.robot_data_.get_joint_vel()
-        qj = q_mj[self.lab2mj] - self.default_angles_lab
-        dqj = dq_mj[self.lab2mj]
-
-        proprio = np.concatenate([
-            ang_vel,
-            gravity_init,
-            command,
-            qj,
-            dqj,
-            self.last_actions_,
-            gait,
-        ])
-
-        if self.is_first_obs_:
-            for i in range(self.num_hist_):
-                start_idx = i * self.obs_size_
-                end_idx = start_idx + self.obs_size_
-                self.proprio_hist_buf_[start_idx:end_idx] = proprio
-            self.is_first_obs_ = False
-        else:
-            shift_size = (self.num_hist_ - 1) * self.obs_size_
-            self.proprio_hist_buf_[:shift_size] = self.proprio_hist_buf_[self.obs_size_:]
-            self.proprio_hist_buf_[shift_size:] = proprio
-
-        self.observations_ = np.clip(self.proprio_hist_buf_, -self.clip_obs_, self.clip_obs_)
-
-    def compute_actions(self):
-        if self.ort_session_ is None:
-            return
-
-        try:
-            input_data = self.observations_.reshape(1, -1).astype(np.float32)
-            input_name = self.ort_session_.get_inputs()[0].name
-            outputs = self.ort_session_.run(None, {input_name: input_data})
-            output_data = outputs[0][0]
-            for i in range(self.action_num_):
-                self.actions_[i] = np.clip(output_data[i], -self.clip_act_, self.clip_act_)
-
-            if self.is_first_action_:
-                print(f"[{self.log_prefix}-ONNX] First Observation:")
-                for i in range(self.obs_size_):
-                    print(f"{self.observations_[i]:.6f} ", end="")
-                print()
-                self.is_first_action_ = False
-        except Exception as e:
-            print(f"[{self.log_prefix}] ONNX Runtime inference error: {e}")
-
-    def on_exit(self):
-        print(f"[{self.log_prefix}] exit")
-        if getattr(self, "obs_log_file", None) is not None:
-            try:
-                self.obs_log_file.flush()
-                self.obs_log_file.close()
-                print(f"[{self.log_prefix}] obs log saved to {self.obs_log_path}")
-            except Exception as e:
-                print(f"[{self.log_prefix}] failed to close obs log: {e}")
-            self.obs_log_file = None
-
-    def check_transition(self, flag: ControlFlag) -> FSMStateName:
-        if flag.fsm_state_command == "gotoSTOP":
-            return FSMStateName.STOP
-        if flag.fsm_state_command == "gotoWALKAMP":
-            return FSMStateName.WALKAMP
-        if flag.fsm_state_command == "gotoMYPOLICY":
-            return FSMStateName.MYPOLICY
-        if flag.fsm_state_command == "gotoXSIMRUN":
-            return FSMStateName.XSIMRUN
-        if flag.fsm_state_command == "gotoZERO":
-            return FSMStateName.ZERO
-        return None
-
-    @staticmethod
-    def euler_to_quaternion_scipy(roll, pitch, yaw, degrees=False):
-        r = Rotation.from_euler("xyz", [roll, pitch, yaw], degrees=degrees)
-        q_xyzw = r.as_quat()
-        return np.array([q_xyzw[3], q_xyzw[0], q_xyzw[1], q_xyzw[2]], dtype=np.float32)
-
-    @staticmethod
-    def quat_rotate_inverse_numpy(q_xyzw, v):
-        q_w = q_xyzw[3]
-        q_v = q_xyzw[:3]
-        a = v * (2.0 * q_w * q_w - 1.0)
-        b = np.cross(q_v, v) * (2.0 * q_w)
-        c = q_v * (2.0 * np.dot(q_v, v))
-        return a - b + c

Deploy_Tienkung/policy/stop/fsm_stop.py

@@ -82,10 +82,6 @@ class FSMStateStop(FSMState):
             return FSMStateName.ZERO
         elif flag.fsm_state_command == "gotoWALKAMP":
             return FSMStateName.WALKAMP
-        elif flag.fsm_state_command == "gotoMYPOLICY":
-            return FSMStateName.MYPOLICY
-        elif flag.fsm_state_command == "gotoXSIMRUN":
-            return FSMStateName.XSIMRUN
         elif flag.fsm_state_command == "gotoBEYONDZERO":
             return FSMStateName.BEYONDZERO
         else:

Deploy_Tienkung/policy/walk_amp/fsm_walkamp.py

@@ -446,8 +446,6 @@ class FSMStateWALKAMP(FSMState):
             return FSMStateName.STOP
         elif flag.fsm_state_command == "gotoWALKAMP":
             return FSMStateName.WALKAMP
-        elif flag.fsm_state_command == "gotoXSIMRUN":
-            return FSMStateName.XSIMRUN
         elif flag.fsm_state_command == "gotoZERO":
             return FSMStateName.ZERO
         else:

Deploy_Tienkung/policy/xsim_run/config/xsim_run.yaml

@@ -1,82 +0,0 @@
-model_path: "../mypolicy/model/policy.onnx"
-motor_num: 29
-actions_size: 23
-dt: 0.01
-warm_start_time: 0.0
-command_clip: 1.0
-
-sim:
-  mujoco_timestep: 0.005
-
-joint_names: [
-  hip_pitch_l_joint, hip_pitch_r_joint, waist_yaw_joint,
-  hip_roll_l_joint, hip_roll_r_joint, waist_roll_joint,
-  hip_yaw_l_joint, hip_yaw_r_joint, waist_pitch_joint,
-  knee_pitch_l_joint, knee_pitch_r_joint,
-  shoulder_pitch_l_joint, shoulder_pitch_r_joint,
-  ankle_pitch_l_joint, ankle_pitch_r_joint,
-  shoulder_roll_l_joint, shoulder_roll_r_joint,
-  ankle_roll_l_joint, ankle_roll_r_joint,
-  shoulder_yaw_l_joint, shoulder_yaw_r_joint,
-  elbow_pitch_l_joint, elbow_pitch_r_joint
-]
-
-control:
-  action_scale: 0.25
-  decimation: 2
-
-gait:
-  gait_air_ratio_l: 0.6
-  gait_air_ratio_r: 0.6
-  gait_phase_offset_l: 0.6
-  gait_phase_offset_r: 0.1
-  gait_cycle: 0.64
-
-normalization:
-  clip_scales:
-    clip_observations: 100.0
-    clip_actions: 100.0
-
-size:
-  num_hist: 10
-  observations_size: 84
-
-gains:
-  kp: [
-    300.0, 300.0, 400.0,
-    300.0, 300.0, 400.0,
-    150.0, 150.0, 400.0,
-    350.0, 350.0,
-    150.0, 150.0,
-    30.0, 30.0,
-    50.0, 50.0,
-    16.8, 16.8,
-    50.0, 50.0,
-    150.0, 150.0
-  ]
-  kd: [
-    10.0, 10.0, 5.0,
-    10.0, 10.0, 10.0,
-    5.0, 5.0, 10.0,
-    10.0, 10.0,
-    7.5, 7.5,
-    2.5, 2.5,
-    2.5, 2.5,
-    1.4, 1.4,
-    2.5, 2.5,
-    5.0, 5.0
-  ]
-
-init_state:
-  default_joint_angles: [
-    0.0, 0.0, 0.0,
-    -0.5, -0.5, 0.0,
-    0.0, 0.0, 0.0,
-    1.0, 1.0,
-    0.0, 0.0,
-    -0.5, -0.5,
-    0.2, -0.2,
-    0.0, 0.0,
-    0.0, 0.0,
-    -0.3, -0.3
-  ]

Deploy_Tienkung/policy/xsim_run/fsm_xsim_run.py

@@ -1,303 +0,0 @@
-"""
-FSM state implementation for an xSIM MuJoCo run policy that follows the
-TienKung-Lab sim2sim observation/action flow more closely.
-"""
-
-import os
-
-import numpy as np
-import onnxruntime as ort
-import yaml
-from scipy.spatial.transform import Rotation
-
-from FSM.fsm_base import FSMState, FSMStateName
-from common.joystick import ControlFlag
-from common.robot_data import RobotData
-
-
-class FSMStateXSIMRUN(FSMState):
-    """Direct-position run policy for xSIM MuJoCo."""
-
-    def __init__(self, robot_data: RobotData):
-        super().__init__(robot_data)
-        self.current_state_name = FSMStateName.XSIMRUN
-        self.log_prefix = "FSMStateXSIMRUN"
-
-        current_dir = os.path.dirname(os.path.abspath(__file__))
-        config_path = os.path.join(current_dir, "config", "xsim_run.yaml")
-        with open(config_path, "r", encoding="utf-8") as f:
-            policy_config = yaml.safe_load(f)
-
-        self.action_num_ = int(policy_config["actions_size"])
-        self.motor_num_ = int(policy_config["motor_num"])
-        self.dt_ = float(policy_config["dt"])
-        self.command_clip_ = float(policy_config.get("command_clip", 1.0))
-
-        size_config = policy_config.get("size", {})
-        self.num_hist_ = int(size_config["num_hist"])
-        self.obs_size_ = int(size_config["observations_size"])
-
-        control_config = policy_config.get("control", {})
-        self.action_scale_ = float(control_config["action_scale"])
-        self.decimation_ = int(control_config["decimation"])
-        self.warm_start_time_ = float(
-            control_config.get(
-                "warm_start_time",
-                policy_config.get("warm_start_time", 0.0),
-            )
-        )
-
-        sim_config = policy_config.get("sim", {})
-        self.mujoco_timestep_ = float(sim_config.get("mujoco_timestep", 0.005))
-        self.policy_period_ = self.dt_ * self.decimation_
-
-        gait_config = policy_config.get("gait", {})
-        self.gait_cycle_ = float(gait_config["gait_cycle"])
-        self.phase_ratio_ = np.array(
-            [
-                gait_config["gait_air_ratio_l"],
-                gait_config["gait_air_ratio_r"],
-            ],
-            dtype=np.float32,
-        )
-        self.phase_offset_ = np.array(
-            [
-                gait_config["gait_phase_offset_l"],
-                gait_config["gait_phase_offset_r"],
-            ],
-            dtype=np.float32,
-        )
-
-        norm_config = policy_config.get("normalization", {})
-        clip_config = norm_config.get("clip_scales", {})
-        self.clip_obs_ = float(clip_config.get("clip_observations", 100.0))
-        self.clip_act_ = float(clip_config.get("clip_actions", 100.0))
-
-        self.default_angles_lab_ = np.array(
-            policy_config["init_state"]["default_joint_angles"],
-            dtype=np.float32,
-        )
-        self.stiffness_lab_ = np.array(policy_config["gains"]["kp"], dtype=np.float32)
-        self.damping_lab_ = np.array(policy_config["gains"]["kd"], dtype=np.float32)
-
-        model_rel_path = policy_config["model_path"]
-        self.model_path_ = os.path.normpath(os.path.join(current_dir, model_rel_path))
-        self._init_onnx_session()
-
-        # sim2sim.py uses policy output in Isaac order and then maps to MuJoCo order.
-        self.mujoco_to_policy_idx_ = np.array(
-            [0, 6, 12, 1, 7, 13, 2, 8, 14, 3, 9, 15, 19, 4, 10, 16, 20, 5, 11, 17, 21, 18, 22],
-            dtype=int,
-        )
-        self.policy_to_mujoco_idx_ = np.array(
-            [0, 3, 6, 9, 13, 17, 1, 4, 7, 10, 14, 18, 2, 5, 8, 11, 15, 19, 21, 12, 16, 20, 22],
-            dtype=int,
-        )
-
-        # RobotData stores 29 joints in leg -> waist -> arm order.
-        self.mujoco_control_indices_ = np.array(
-            [
-                0, 1, 2, 3, 4, 5,
-                6, 7, 8, 9, 10, 11,
-                12, 13, 14,
-                15, 16, 17, 18,
-                22, 23, 24, 25,
-            ],
-            dtype=int,
-        )
-
-        self.default_angles_mujoco23_ = self.default_angles_lab_[self.policy_to_mujoco_idx_]
-        self.observations_ = np.zeros(self.obs_size_ * self.num_hist_, dtype=np.float32)
-        self.obs_history_ = np.zeros_like(self.observations_)
-        self.actions_ = np.zeros(self.action_num_, dtype=np.float32)
-        self.last_actions_ = np.zeros(self.action_num_, dtype=np.float32)
-        self.current_gait_ = np.zeros(6, dtype=np.float32)
-        self.hold_pose_29_ = np.zeros(self.motor_num_, dtype=np.float32)
-        self._warm_start_pose_29_ = np.zeros(self.motor_num_, dtype=np.float32)
-        self._first_obs = True
-        self._policy_step_counter = 0
-        self.waiting_for_motion_ = True
-        self.motion_threshold_ = 1e-3
-
-        if self.warm_start_time_ > 0 and self.policy_period_ > 0:
-            self._warm_start_steps = max(1, int(self.warm_start_time_ / self.policy_period_))
-        else:
-            self._warm_start_steps = 0
-        self._warmup_inference_counter = 0
-
-        self.kp_29_ = np.zeros(self.motor_num_, dtype=np.float32)
-        self.kd_29_ = np.zeros(self.motor_num_, dtype=np.float32)
-        for lab_idx, mj_idx in enumerate(self.mujoco_control_indices_[self.policy_to_mujoco_idx_]):
-            self.kp_29_[mj_idx] = self.stiffness_lab_[lab_idx]
-            self.kd_29_[mj_idx] = self.damping_lab_[lab_idx]
-
-    def _init_onnx_session(self) -> None:
-        options = ort.SessionOptions()
-        options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
-        options.intra_op_num_threads = 1
-        options.inter_op_num_threads = 1
-        self.ort_session_ = ort.InferenceSession(
-            self.model_path_,
-            options,
-            providers=["CPUExecutionProvider"],
-        )
-        print(f"[{self.log_prefix}] ONNX model loaded: {self.model_path_}")
-
-    def on_enter(self):
-        self.observations_.fill(0.0)
-        self.obs_history_.fill(0.0)
-        self.actions_.fill(0.0)
-        self.last_actions_.fill(0.0)
-        self.current_gait_.fill(0.0)
-        self._first_obs = True
-        self._policy_step_counter = 0
-        self._warmup_inference_counter = 0
-        self.waiting_for_motion_ = True
-
-        current_q = self.robot_data_.get_joint_pos().copy()
-        self.hold_pose_29_ = current_q
-        self._warm_start_pose_29_ = current_q
-
-        base = self.robot_data_.q_d_.shape[0] - self.motor_num_
-        self.robot_data_.q_d_[base:base + self.motor_num_] = current_q
-        self.robot_data_.q_dot_d_[base:base + self.motor_num_] = 0.0
-        self.robot_data_.tau_d_[base:base + self.motor_num_] = 0.0
-        self.robot_data_.joint_kp_p_[:self.motor_num_] = self.kp_29_
-        self.robot_data_.joint_kd_p_[:self.motor_num_] = self.kd_29_
-        print(f"[{self.log_prefix}] enter")
-
-    def run(self, flag: ControlFlag):
-        walk_cmd = np.clip(
-            np.array(self.robot_data_.get_walk_cmd(), dtype=np.float32),
-            -self.command_clip_,
-            self.command_clip_,
-        )
-        base = self.robot_data_.q_d_.shape[0] - self.motor_num_
-
-        if self.waiting_for_motion_:
-            if np.max(np.abs(walk_cmd)) <= self.motion_threshold_:
-                self.robot_data_.q_d_[base:base + self.motor_num_] = self.hold_pose_29_
-                self.robot_data_.q_dot_d_[base:base + self.motor_num_] = 0.0
-                self.robot_data_.tau_d_[base:base + self.motor_num_] = 0.0
-                self.robot_data_.joint_kp_p_[:self.motor_num_] = self.kp_29_
-                self.robot_data_.joint_kd_p_[:self.motor_num_] = self.kd_29_
-                return
-            self.waiting_for_motion_ = False
-            self._warm_start_pose_29_ = self.robot_data_.get_joint_pos().copy()
-            print(f"[{self.log_prefix}] motion command detected: {walk_cmd}")
-
-        if int(self.robot_data_.time_now_ / self.dt_) % self.decimation_ == 0:
-            self.current_gait_ = self._compute_gait_features()
-            self.compute_observation(walk_cmd)
-            self.compute_actions()
-
-            target_mujoco23 = (
-                self.actions_[self.policy_to_mujoco_idx_] * self.action_scale_
-                + self.default_angles_mujoco23_
-            )
-            target_q_29 = self.hold_pose_29_.copy()
-            target_q_29[self.mujoco_control_indices_] = target_mujoco23
-
-            commanded_q_29 = target_q_29
-            if self._warm_start_steps > 0 and self._warmup_inference_counter < self._warm_start_steps:
-                self._warmup_inference_counter += 1
-                blend = self._warmup_inference_counter / float(self._warm_start_steps)
-                commanded_q_29 = (1.0 - blend) * self._warm_start_pose_29_ + blend * target_q_29
-
-            self.robot_data_.q_d_[base:base + self.motor_num_] = commanded_q_29
-            self.robot_data_.q_dot_d_[base:base + self.motor_num_] = 0.0
-            self.robot_data_.tau_d_[base:base + self.motor_num_] = 0.0
-            self.robot_data_.joint_kp_p_[:self.motor_num_] = self.kp_29_
-            self.robot_data_.joint_kd_p_[:self.motor_num_] = self.kd_29_
-            self.last_actions_[:] = self.actions_
-
-    def _compute_gait_features(self) -> np.ndarray:
-        t = self._policy_step_counter * self.policy_period_ / self.gait_cycle_
-        gait_phase = (t + self.phase_offset_) % 1.0
-        self._policy_step_counter += 1
-        return np.concatenate(
-            [
-                np.sin(2.0 * np.pi * gait_phase),
-                np.cos(2.0 * np.pi * gait_phase),
-                self.phase_ratio_,
-            ],
-            axis=0,
-        ).astype(np.float32)
-
-    def compute_observation(self, walk_cmd: np.ndarray):
-        if np.linalg.norm(self.robot_data_.imu_quat_) > 0.0:
-            q_wxyz = self.robot_data_.imu_quat_.astype(np.float32)
-            q_xyzw = np.array([q_wxyz[1], q_wxyz[2], q_wxyz[3], q_wxyz[0]], dtype=np.float32)
-        else:
-            roll = float(self.robot_data_.imu_data_[2])
-            pitch = float(self.robot_data_.imu_data_[1])
-            yaw = float(self.robot_data_.imu_data_[0])
-            q_wxyz = self.euler_to_quaternion_scipy(roll, pitch, yaw)
-            q_xyzw = np.array([q_wxyz[1], q_wxyz[2], q_wxyz[3], q_wxyz[0]], dtype=np.float32)
-
-        gravity = self.quat_rotate_inverse_numpy(q_xyzw, np.array([0.0, 0.0, -1.0], dtype=np.float32))
-        q_29 = self.robot_data_.get_joint_pos()
-        dq_29 = self.robot_data_.get_joint_vel()
-        q_23 = q_29[self.mujoco_control_indices_]
-        dq_23 = dq_29[self.mujoco_control_indices_]
-
-        proprio = np.concatenate(
-            [
-                self.robot_data_.get_angular_velocity(),
-                gravity,
-                walk_cmd,
-                (q_23 - self.default_angles_mujoco23_)[self.mujoco_to_policy_idx_],
-                dq_23[self.mujoco_to_policy_idx_],
-                np.clip(self.last_actions_, -self.clip_act_, self.clip_act_),
-                self.current_gait_,
-            ],
-            axis=0,
-        ).astype(np.float32)
-
-        if self._first_obs:
-            for i in range(self.num_hist_):
-                start = i * self.obs_size_
-                self.obs_history_[start:start + self.obs_size_] = proprio
-            self._first_obs = False
-        else:
-            self.obs_history_ = np.roll(self.obs_history_, -self.obs_size_)
-            self.obs_history_[-self.obs_size_:] = proprio
-
-        self.observations_ = np.clip(self.obs_history_, -self.clip_obs_, self.clip_obs_)
-
-    def compute_actions(self):
-        input_name = self.ort_session_.get_inputs()[0].name
-        input_data = self.observations_.reshape(1, -1).astype(np.float32)
-        outputs = self.ort_session_.run(None, {input_name: input_data})
-        self.actions_[:] = np.clip(outputs[0][0][: self.action_num_], -self.clip_act_, self.clip_act_)
-
-    def on_exit(self):
-        print(f"[{self.log_prefix}] exit")
-
-    def check_transition(self, flag: ControlFlag) -> FSMStateName:
-        if flag.fsm_state_command == "gotoSTOP":
-            return FSMStateName.STOP
-        if flag.fsm_state_command == "gotoZERO":
-            return FSMStateName.ZERO
-        if flag.fsm_state_command == "gotoWALKAMP":
-            return FSMStateName.WALKAMP
-        if flag.fsm_state_command == "gotoMYPOLICY":
-            return FSMStateName.MYPOLICY
-        if flag.fsm_state_command == "gotoXSIMRUN":
-            return FSMStateName.XSIMRUN
-        return None
-
-    @staticmethod
-    def euler_to_quaternion_scipy(roll, pitch, yaw, degrees=False):
-        r = Rotation.from_euler("xyz", [roll, pitch, yaw], degrees=degrees)
-        q_xyzw = r.as_quat()
-        return np.array([q_xyzw[3], q_xyzw[0], q_xyzw[1], q_xyzw[2]], dtype=np.float32)
-
-    @staticmethod
-    def quat_rotate_inverse_numpy(q_xyzw, v):
-        q_w = q_xyzw[3]
-        q_v = q_xyzw[:3]
-        a = v * (2.0 * q_w * q_w - 1.0)
-        b = np.cross(q_v, v) * (2.0 * q_w)
-        c = q_v * (2.0 * np.dot(q_v, v))
-        return a - b + c

Deploy_Tienkung/policy/zero/fsm_zero.py

@@ -75,10 +75,6 @@ class FSMStateZero(FSMState):
             return FSMStateName.STOP
         elif flag.fsm_state_command == "gotoWALKAMP":
             return FSMStateName.WALKAMP
-        elif flag.fsm_state_command == "gotoMYPOLICY":
-            return FSMStateName.MYPOLICY
-        elif flag.fsm_state_command == "gotoXSIMRUN":
-            return FSMStateName.XSIMRUN
         elif flag.fsm_state_command == "gotoZERO":
             return FSMStateName.ZERO
         elif flag.fsm_state_command == "gotoBEYONDZERO":

Deploy_Tienkung/rl_control_node.py

@@ -374,8 +374,6 @@ class XMIGCSControlNode(Node):
             FSMStateName.STOP: "gotoSTOP",
             FSMStateName.ZERO: "gotoZERO",
             FSMStateName.WALKAMP: "gotoWALKAMP",
-            FSMStateName.MYPOLICY: "gotoMYPOLICY",
-            FSMStateName.XSIMRUN: "gotoXSIMRUN",
         }
         current_state = self.robot_fsm.get_current_state()
         return state_to_command.get(current_state, self.control_flag.fsm_state_command)

Deploy_Tienkung/udp_loopback/README.md

@@ -84,7 +84,6 @@ python3 udp_loopback/udp_xbox_sender.py
 - `A -> mode_stride -> gotoWALKAMP`
 - `X -> pose_home -> gotoZERO`
 - `Y -> pose_hold -> gotoSTOP`
-- `B -> mode_dash -> gotoMYPOLICY`
 - `START -> trim_reset`
 - 左摇杆 Y -> 连续前后速度
 - 左摇杆 X -> 连续横移速度
@@ -96,7 +95,6 @@ python3 udp_loopback/udp_xbox_sender.py
 - `pose_home -> gotoZERO`
 - `pose_hold -> gotoSTOP`
 - `mode_stride -> gotoWALKAMP`
-- `mode_dash -> gotoMYPOLICY`
 - `surge/sway/spin -> x/y/yaw speed command`
 
 当前事件码不是原工程里的 `gotoZERO` / `gotoSTOP` / `x_speed_command` 这一套，而是：
@@ -104,7 +102,6 @@ python3 udp_loopback/udp_xbox_sender.py
 - `pose_home`
 - `pose_hold`
 - `mode_stride`
-- `mode_dash`
 - `surge_up`
 - `surge_down`
 - `sway_left`

Deploy_Tienkung/udp_loopback/udp_fsm_controller.py

@@ -161,12 +161,6 @@ class UDPFSMController:
             elif event_code == "mode_stride":
                 self.motion_frame.mode_tag = "mode_stride"
                 self.last_fsm_command_time = packet.sent_at
-            elif event_code == "mode_dash":
-                self.motion_frame.mode_tag = "mode_dash"
-                self.last_fsm_command_time = packet.sent_at
-            elif event_code == "mode_xrun":
-                self.motion_frame.mode_tag = "mode_xrun"
-                self.last_fsm_command_time = packet.sent_at
             elif event_code == "surge_up":
                 self.motion_frame.surge_goal = min(
                     self.max_surge, self.motion_frame.surge_goal + self.surge_step
@@ -236,8 +230,6 @@ class UDPFSMController:
             "pose_home": "gotoZERO",
             "pose_hold": "gotoSTOP",
             "mode_stride": "gotoWALKAMP",
-            "mode_dash": "gotoMYPOLICY",
-            "mode_xrun": "gotoXSIMRUN",
         }
         self.udp_flag.enable = self.motion_frame.relay_on
         self.udp_flag.fsm_state_command = mode_to_fsm_command.get(

Deploy_Tienkung/udp_loopback/udp_keyboard_sender.py

@@ -56,8 +56,6 @@ class UDPKeyboardSender:
         print("  z -> pose_home")
         print("  c -> pose_hold")
         print("  m -> mode_stride")
-        print("  p -> mode_dash")
-        print("  n -> mode_xrun")
         print("  w/s -> surge +/-")
         print("  a/d -> sway +/-")
         print("  q/e -> spin +/-")
@@ -118,8 +116,6 @@ class UDPKeyboardSender:
             "z": ("pose_home", "z", 1.0),
             "c": ("pose_hold", "c", 1.0),
             "m": ("mode_stride", "m", 1.0),
-            "p": ("mode_dash", "p", 1.0),
-            "n": ("mode_xrun", "n", 1.0),
             "r": ("trim_reset", "r", 1.0),
             "4": ("set_surge", "4", 0.0),
             "5": ("set_sway", "5", 0.0),

Deploy_Tienkung/udp_loopback/udp_loopback_node.py

@@ -138,10 +138,6 @@ class UDPLoopbackNode:
                 self.motion_frame.mode_tag = "pose_hold"
             elif event_code == "mode_stride":
                 self.motion_frame.mode_tag = "mode_stride"
-            elif event_code == "mode_dash":
-                self.motion_frame.mode_tag = "mode_dash"
-            elif event_code == "mode_xrun":
-                self.motion_frame.mode_tag = "mode_xrun"
             elif event_code == "surge_up":
                 self.motion_frame.surge_goal = min(
                     self.max_surge, self.motion_frame.surge_goal + self.surge_step

Deploy_Tienkung/udp_loopback/udp_xbox_sender.py

@@ -44,7 +44,7 @@ class UDPXboxSender(Node):
             f"Forwarding {self.joy_topic} -> udp://{self.target_host}:{self.target_port}"
         )
         self.get_logger().info(
-            "Buttons: A=WALKAMP X=ZERO Y=STOP B=MYPOLICY START=reset"
+            "Buttons: A=WALKAMP X=ZERO Y=STOP START=reset"
         )
 
     def destroy_node(self) -> bool:
@@ -167,13 +167,6 @@ class UDPXboxSender(Node):
             self._send_event("pose_home", "x")
         elif self._rising_edge(state, "a"):
             self._send_event("mode_stride", "a")
-        elif self._rising_edge(state, "b"):
-            self._send_event("mode_dash", "b")
-        elif (
-            self._rising_edge(state, "home")
-            and state["l_trigger"] < self.trigger_pressed_threshold
-        ):
-            self._send_event("mode_xrun", "home")
 
     def _send_trim_event(self, state: Dict[str, float]) -> None:
         if self._rising_edge(state, "start"):