diff --git a/README.md b/README.md
index c56867f..1cd423b 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
@@ -196,4 +196,3 @@ If you find our work helpful, please cite:
New contributions follow the [MIT License](LICENSE); the original unitree_rl_gym remains under the [BSD 3-Clause License](LICENSE).
See the complete [LICENSE file](LICENSE) for details.
-
diff --git a/README_zh.md b/README_zh.md
index 68e1de9..2cda1a0 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -1,7 +1,7 @@
@@ -197,4 +197,3 @@ python deploy_real_go2.py eth0
详情请阅读完整 [LICENSE 文件](./LICENSE)。
-
diff --git a/deploy/deploy_mujoco/deploy_go2_moe.py b/deploy/deploy_mujoco/deploy_go2_moe.py
deleted file mode 100644
index 78b9aed..0000000
--- a/deploy/deploy_mujoco/deploy_go2_moe.py
+++ /dev/null
@@ -1,272 +0,0 @@
-import time
-import mujoco.viewer
-import mujoco
-import numpy as np
-from legged_gym import LEGGED_GYM_ROOT_DIR
-import torch
-import yaml
-import os
-import imageio
-from pathlib import Path
-from argparse import ArgumentParser
-import pygame
-# from matplotlib import pyplot as plt # 移除 matplotlib
-
-def get_gravity_orientation(quaternion):
- qw = quaternion[0]
- qx = quaternion[1]
- qy = quaternion[2]
- qz = quaternion[3]
-
- gravity_orientation = np.zeros(3)
-
- gravity_orientation[0] = 2 * (-qz * qx + qw * qy)
- gravity_orientation[1] = -2 * (qz * qy + qw * qx)
- gravity_orientation[2] = 1 - 2 * (qw * qw + qz * qz)
-
- return gravity_orientation
-
-
-def pd_control(target_q, q, kp, target_dq, dq, kd):
- """Calculates torques from position commands"""
- return (target_q - q) * kp + (target_dq - dq) * kd
-
-def get_xbox_command(joystick, max_cmd):
- # 注意：如果开启了 Pygame 显示窗口，这里 event.pump 也是必要的
- pygame.event.pump()
- dead_zone = 0.1
- if joystick is not None:
- lx = joystick.get_axis(0)
- ly = joystick.get_axis(1)
- rx = joystick.get_axis(3)
- if abs(lx) < dead_zone: lx = 0
- if abs(ly) < dead_zone: ly = 0
- if abs(rx) < dead_zone: rx = 0
- cmd_x = -ly * max_cmd[0]
- cmd_y = -lx * max_cmd[1]
- cmd_yaw = -rx * max_cmd[2]
- return np.array([cmd_x, cmd_y, cmd_yaw], dtype=np.float32)
- return np.zeros(3, dtype=np.float32)
-
-def draw_moe_weights(screen, weights, width, height):
- """使用 Pygame 绘制 MoE 权重"""
- screen.fill((255, 255, 255)) # 白底
-
- num_experts = len(weights)
- if num_experts == 0:
- return
-
- # 设置边距
- margin = 5
- bar_width = (width - 2 * margin) / num_experts
- max_bar_height = height - 2 * margin
-
- for i, w in enumerate(weights):
- # 限制 w 在 [0, 1] 之间用于显示
- w_clamped = max(0.0, min(1.0, w))
- bar_height = int(w_clamped * max_bar_height)
-
- # 计算矩形位置 (Pygame 坐标原点在左上角)
- # left, top, width, height
- x = margin + i * bar_width
- y = height - margin - bar_height # 从底部向上长
-
- # 绘制矩形 (蓝色)
- # 在 bar 之间留一点空隙 (width - 2)
- pygame.draw.rect(screen, (50, 100, 255), (x, y, bar_width - 2, bar_height))
-
- pygame.display.flip()
-
-if __name__ == "__main__":
- parser = ArgumentParser()
- parser.add_argument("--save-video", action="store_true", help="Whether to save video of the simulation.")
- parser.add_argument("--visualize-moe-weights", action="store_true", help="Whether to visualize mixture of experts weights.")
- args = parser.parse_args()
- save_video = args.save_video
- visualize_moe_weights = args.visualize_moe_weights
- config_file = "go2.yaml"
-
- # Pygame 初始化
- pygame.init()
-
- use_joystick = False
- joystick = None
- if pygame.joystick.get_count() > 0:
- joystick = pygame.joystick.Joystick(0)
- joystick.init()
- use_joystick = True
- print(f"Detected Joystick: {joystick.get_name()}")
- else:
- print("No Joystick detected. Using default commands from config.")
-
- # 如果需要可视化权重，设置 Pygame 窗口
- screen = None
- win_width, win_height = 400, 200
- if visualize_moe_weights:
- # 创建一个独立的窗口用于显示权重
- screen = pygame.display.set_mode((win_width, win_height))
- pygame.display.set_caption("MoE Weights Visualization")
-
- with open(f"{LEGGED_GYM_ROOT_DIR}/deploy/deploy_mujoco/configs/{config_file}", "r") as f:
- config = yaml.load(f, Loader=yaml.FullLoader)
- policy_path = config["policy_path"].replace("{LEGGED_GYM_ROOT_DIR}", LEGGED_GYM_ROOT_DIR)
- xml_path = config["xml_path"].replace("{LEGGED_GYM_ROOT_DIR}", LEGGED_GYM_ROOT_DIR)
-
- simulation_duration = config["simulation_duration"]
- simulation_dt = config["simulation_dt"]
- control_decimation = config["control_decimation"]
-
- kps = np.array(config["kps"], dtype=np.float32)
- kds = np.array(config["kds"], dtype=np.float32)
-
- default_angles = np.array(config["default_angles"], dtype=np.float32)
-
- lin_vel_scale = config["lin_vel_scale"]
- ang_vel_scale = config["ang_vel_scale"]
- dof_pos_scale = config["dof_pos_scale"]
- dof_vel_scale = config["dof_vel_scale"]
- action_scale = config["action_scale"]
- cmd_scale = np.array(config["cmd_scale"], dtype=np.float32)
-
- num_actions = config["num_actions"]
- num_obs = config["num_obs"]
-
- cmd = np.array(config["cmd_init"], dtype=np.float32)
-
- idx_model2mj = idx_mj2model = list(range(num_actions))
- if 'mujoco_joint_names' in config and 'model_joint_names' in config:
- mujoco_joint_names = config["mujoco_joint_names"]
- model_joint_names = config["model_joint_names"]
- idx_model2mj = [model_joint_names.index(joint) for joint in mujoco_joint_names]
- idx_mj2model = [mujoco_joint_names.index(joint) for joint in model_joint_names]
-
- video_save_dir = str(Path(__file__).parent / "videos")
- os.makedirs(video_save_dir, exist_ok=True)
-
- model_name = os.path.basename(policy_path).split('.')[0]
- cmd_str = f"cmd_{cmd[0]}_{cmd[1]}_{cmd[2]}"
- video_filename = f"{model_name}_{cmd_str}.mp4"
- video_path = os.path.join(video_save_dir, video_filename)
- print(f"Video recording will be saved to: {video_path}")
-
- # define context variables
- action = np.zeros(num_actions, dtype=np.float32)
- last_action = np.zeros(num_actions, dtype=np.float32)
- target_dof_pos = default_angles.copy()
- obs = np.zeros(num_obs, dtype=np.float32)
-
- counter = 0
-
- # Load robot model
- m = mujoco.MjModel.from_xml_path(xml_path)
- d = mujoco.MjData(m)
- m.opt.timestep = simulation_dt
-
- renderer = mujoco.Renderer(m, height=360, width=640)
-
- # load policy
- policy = torch.jit.load(policy_path)
-
- if save_video:
- video_fps = 50
- sim_fps = 1.0 / m.opt.timestep
- frame_skip = int(sim_fps / video_fps)
- if frame_skip < 1:
- frame_skip = 1
- writer = imageio.get_writer(video_path, fps=video_fps)
- print(f"Sim FPS: {sim_fps:.2f}, Video FPS: {video_fps}, Frame Skip: {frame_skip}, Save at: {video_path}")
-
- # 移除了 plt 初始化逻辑
-
- with mujoco.viewer.launch_passive(m, d) as viewer:
-
- # set viewer.camera to follow robot
- viewer.cam.type = mujoco.mjtCamera.mjCAMERA_TRACKING
- viewer.cam.trackbodyid = 1
- viewer.cam.distance = 3.0
- viewer.cam.elevation = -30.0
- viewer.cam.azimuth = 0.0
-
- # Close the viewer automatically after simulation_duration wall-seconds.
- start = time.time()
- while viewer.is_running() and time.time() - start < simulation_duration:
- step_start = time.time()
-
- if use_joystick and counter % control_decimation == 0:
- cmd = get_xbox_command(joystick, config["max_cmd"])
- print(f"Cmd: Vx={cmd[0]:.2f}, Vy={cmd[1]:.2f}, Wz={cmd[2]:.2f}", end='\r')
- elif visualize_moe_weights and counter % control_decimation == 0:
- # 如果没有手柄但开了可视化，也需要 pump 事件，防止窗口卡死
- pygame.event.pump()
-
- tau = pd_control(target_dof_pos, d.qpos[7:], kps, np.zeros_like(kds), d.qvel[6:], kds)
- d.ctrl[:] = tau
-
- mujoco.mj_step(m, d)
-
- if save_video and counter % frame_skip == 0:
- try:
- renderer.update_scene(d, camera=viewer.cam)
- frame = renderer.render()
- writer.append_data(frame)
- except Exception as e:
- print(f"Error rendering frame: {e}")
-
- counter += 1
- if counter % control_decimation == 0:
- # Apply control signal here.
-
- # create observation
- qj = d.qpos[7:]
- dqj = d.qvel[6:]
- quat = d.qpos[3:7]
- lin_vel = d.qvel[:3]
- ang_vel = d.qvel[3:6]
-
- qj = (qj - default_angles) * dof_pos_scale
-
- dqj = dqj * dof_vel_scale
- gravity_orientation = get_gravity_orientation(quat)
- lin_vel = lin_vel * lin_vel_scale
- ang_vel = ang_vel * ang_vel_scale
-
- obs[:3] = ang_vel
- obs[3:6] = gravity_orientation
- obs[6:9] = cmd * cmd_scale
- obs[9 : 9 + num_actions] = qj[idx_mj2model]
- obs[9 + num_actions : 9 + 2 * num_actions] = dqj[idx_mj2model]
- obs[9 + 2 * num_actions : 9 + 3 * num_actions] = action[idx_mj2model]
- obs_tensor = torch.from_numpy(obs).unsqueeze(0)
- # policy inference
- last_action = action
- result = policy(obs_tensor)
-
- # 处理 MoE 和 绘图
- if isinstance(result, tuple):
- action, (weights, latent) = result # moe
- action = action.detach().numpy().squeeze()[idx_model2mj]
- weights = weights.detach().numpy().squeeze()
-
- if visualize_moe_weights and screen is not None:
- draw_moe_weights(screen, weights, win_width, win_height)
-
- else:
- action = result.detach().numpy().squeeze()[idx_model2mj]
-
- # transform action to target_dof_pos
- target_dof_pos = action * action_scale + default_angles
-
- # Pick up changes to the physics state, apply perturbations, update options from GUI.
- viewer.sync()
-
- # 如果需要严格同步时间，可以解开下面的注释
- # time_until_next_step = m.opt.timestep - (time.time() - step_start)
- # if time_until_next_step > 0:
- # time.sleep(time_until_next_step)
-
- if save_video:
- writer.close()
-
- # 退出时清理 Pygame
- pygame.quit()
- print(f"Video saved successfully to {video_path}")
\ No newline at end of file