diff --git a/UPDATE.md b/UPDATE.md
index 8619c89..bdad085 100644
--- a/UPDATE.md
+++ b/UPDATE.md
@@ -1,6 +1,8 @@
 # 20260107
 ## v0.1.6
 1. 加入`go2_rem_cts`, student使用MoE结构, teacher使用普通CTS, 使用非共享权重和全goal输入
+2. 加入`move_down_by_acuumulated_xy_command`选择是否通过累计速度来降低等级
+3. 加入`dynamic_resample_commands`选择是否通过累计速度来动态调整指令采样下限
 # 20260106
 ## v0.1.5
 1. 加入`go2_ac_moe_cts`, 参考MoELoco将MoE加载Actor-Critic上, 使用非共享权重和全goal输入
diff --git a/legged_gym/envs/base/legged_robot.py b/legged_gym/envs/base/legged_robot.py
index 8cbf698..cc214fe 100644
--- a/legged_gym/envs/base/legged_robot.py
+++ b/legged_gym/envs/base/legged_robot.py
@@ -221,7 +221,7 @@ class LeggedRobot(BaseTask):
         self.episode_length_buf[env_ids] = 0
         self.reset_buf[env_ids] = 1
         self.commands_resampling_step[env_ids] = self.cfg.commands.resampling_time / self.dt
-        self.commands_xy_accummulation[env_ids] = 0.0
+        self.commands_xy_accumulation[env_ids] = 0.0
         if self.cfg.commands.curriculum:
             self.update_command_curriculum(env_ids)
         self._resample_commands(env_ids)
@@ -442,36 +442,47 @@ class LeggedRobot(BaseTask):
                     self.cfg.commands.command_range_curriculum.pop(i)
                     self._update_env_command_ranges()
                     print(f"Command range updated at iter {current_iter}: {self.command_ranges}")
-        # 到达边界0.625倍宽度的剩余距离
-        remaining_dist = torch.clip(0.625 * self.cfg.terrain.terrain_length - torch.norm(self.commands_xy_accummulation[env_ids], dim=1) * self.cfg.commands.resampling_time, 0.0)
-        if ((self.max_episode_length - self.episode_length_buf[env_ids]) == 0).any():
-            raise ValueError("Some envs have zero remaining episode length during command resampling")
-        vel_low_bound = torch.clip(remaining_dist / ((self.max_episode_length - self.episode_length_buf[env_ids] + 1e-9) * self.dt), 0.0)
-        self.commands[env_ids, 0] = sample_disjoint_intervals(
-            env_ids,
-            vel_low_bound,
-            self.env_command_ranges["lin_vel_x"][env_ids, 0],
-            self.env_command_ranges["lin_vel_x"][env_ids, 1],
-            self.device
-        )
-        self.commands[env_ids, 1] = sample_disjoint_intervals(
-            env_ids,
-            vel_low_bound,
-            self.env_command_ranges["lin_vel_y"][env_ids, 0],
-            self.env_command_ranges["lin_vel_y"][env_ids, 1],
-            self.device
-        )
-        if self.cfg.commands.heading_command:
-            r = torch.rand(len(env_ids), device=self.device)
-            lower = self.env_command_ranges["heading"][env_ids, 0]
-            upper = self.env_command_ranges["heading"][env_ids, 1]
-            self.commands[env_ids, 3] = (upper - lower) * r + lower
+        remaining_dist = torch.clip(0.625 * self.cfg.terrain.terrain_length - torch.norm(self.commands_xy_accumulation[env_ids], dim=1) * self.cfg.commands.resampling_time, 0.0)
+        if self.cfg.commands.dynamic_resample_commands:
+            # arrive at boundary 0.625 times the width of the remaining distance
+            if ((self.max_episode_length - self.episode_length_buf[env_ids]) == 0).any():
+                raise ValueError("Some envs have zero remaining episode length during command resampling")
+            vel_low_bound = torch.clip(remaining_dist / ((self.max_episode_length - self.episode_length_buf[env_ids] + 1e-9) * self.dt), 0.0)
+            self.commands[env_ids, 0] = sample_disjoint_intervals(
+                env_ids,
+                vel_low_bound,
+                self.env_command_ranges["lin_vel_x"][env_ids, 0],
+                self.env_command_ranges["lin_vel_x"][env_ids, 1],
+                self.device
+            )
+            self.commands[env_ids, 1] = sample_disjoint_intervals(
+                env_ids,
+                vel_low_bound,
+                self.env_command_ranges["lin_vel_y"][env_ids, 0],
+                self.env_command_ranges["lin_vel_y"][env_ids, 1],
+                self.device
+            )
+            if self.cfg.commands.heading_command:
+                r = torch.rand(len(env_ids), device=self.device)
+                lower = self.env_command_ranges["heading"][env_ids, 0]
+                upper = self.env_command_ranges["heading"][env_ids, 1]
+                self.commands[env_ids, 3] = (upper - lower) * r + lower
+            else:
+                r = torch.rand(len(env_ids), device=self.device)
+                lower = self.env_command_ranges["ang_vel_yaw"][env_ids, 0]
+                upper = self.env_command_ranges["ang_vel_yaw"][env_ids, 1]
+                self.commands[env_ids, 2] = (upper - lower) * r + lower
+            self.commands_resampling_step[env_ids] = self.cfg.commands.resampling_time / self.dt
         else:
-            r = torch.rand(len(env_ids), device=self.device)
-            lower = self.env_command_ranges["ang_vel_yaw"][env_ids, 0]
-            upper = self.env_command_ranges["ang_vel_yaw"][env_ids, 1]
-            self.commands[env_ids, 2] = (upper - lower) * r + lower
-        self.commands_resampling_step[env_ids] = self.cfg.commands.resampling_time / self.dt
+            self.commands[env_ids, 0] = torch_rand_float(self.command_ranges["lin_vel_x"][0], self.command_ranges["lin_vel_x"][1], (len(env_ids), 1), device=self.device).squeeze(1)
+            self.commands[env_ids, 1] = torch_rand_float(self.command_ranges["lin_vel_y"][0], self.command_ranges["lin_vel_y"][1], (len(env_ids), 1), device=self.device).squeeze(1)
+            if self.cfg.commands.heading_command:
+                self.commands[env_ids, 3] = torch_rand_float(self.command_ranges["heading"][0], self.command_ranges["heading"][1], (len(env_ids), 1), device=self.device).squeeze(1)
+            else:
+                self.commands[env_ids, 2] = torch_rand_float(self.command_ranges["ang_vel_yaw"][0], self.command_ranges["ang_vel_yaw"][1], (len(env_ids), 1), device=self.device).squeeze(1)
+
+            # set small commands to zero
+            self.commands[env_ids, :2] *= (torch.norm(self.commands[env_ids, :2], dim=1) > 0.2).unsqueeze(1)
 
         # set small commands to zero
         # self.commands[env_ids, :2] *= (torch.norm(self.commands[env_ids, :2], dim=1) > 0.2).unsqueeze(1)
@@ -559,7 +570,7 @@ class LeggedRobot(BaseTask):
             self.commands[zero_env_ids, :3] = 0.0
             self.stop_heading[zero_env_ids] = True
 
-        self.commands_xy_accummulation[env_ids] += self.commands[env_ids, :2]
+        self.commands_xy_accumulation[env_ids] += self.commands[env_ids, :2]
 
     def _compute_torques(self, actions):
         """ Compute torques from actions.
@@ -782,7 +793,7 @@ class LeggedRobot(BaseTask):
         self.commands = torch.zeros(self.num_envs, self.cfg.commands.num_commands, dtype=torch.float, device=self.device, requires_grad=False) # x vel, y vel, yaw vel, heading
         self.commands_scale = torch.tensor([self.obs_scales.lin_vel, self.obs_scales.lin_vel, self.obs_scales.ang_vel], device=self.device, requires_grad=False,) # TODO change this
         self.commands_resampling_step = torch.zeros(self.num_envs, dtype=torch.float, device=self.device, requires_grad=False)
-        self.commands_xy_accummulation = torch.zeros(self.num_envs, 2, dtype=torch.float, device=self.device, requires_grad=False)
+        self.commands_xy_accumulation = torch.zeros(self.num_envs, 2, dtype=torch.float, device=self.device, requires_grad=False)
         self.zero_command_proba = 0.0
         self.feet_air_time = torch.zeros(self.num_envs, self.feet_indices.shape[0], dtype=torch.float, device=self.device, requires_grad=False)
         self.last_contacts = torch.zeros(self.num_envs, len(self.feet_indices), dtype=torch.bool, device=self.device, requires_grad=False)
@@ -1118,9 +1129,11 @@ class LeggedRobot(BaseTask):
         distance = self.max_move_distance[env_ids]
         # robots that walked far enough progress to harder terains
         move_up = distance > self.terrain.env_length / 2
-        # robots that walked less than half of their required distance go to simpler terrains
-        # move_down = (distance < torch.norm(self.commands[env_ids, :2], dim=1) * self.max_episode_length_s * 0.5) * ~move_up
-        move_down = (distance < torch.norm(self.commands_xy_accummulation[env_ids], dim=1) * (self.cfg.commands.resampling_time * (1 - self.zero_command_proba)) * 0.5) * ~move_up
+        if self.cfg.terrain.move_down_by_acuumulated_xy_command:
+            move_down = (distance < torch.norm(self.commands_xy_accumulation[env_ids], dim=1) * (self.cfg.commands.resampling_time * (1 - self.zero_command_proba)) * 0.5) * ~move_up
+        else:
+            # robots that walked less than half of their required distance go to simpler terrains
+            move_down = (distance < torch.norm(self.commands[env_ids, :2], dim=1) * self.max_episode_length_s * 0.5) * ~move_up
         
         self.terrain_levels[env_ids] += 1 * move_up - 1 * move_down
         # Robots that solve the last level are sent to a random one
diff --git a/legged_gym/envs/base/legged_robot_config.py b/legged_gym/envs/base/legged_robot_config.py
index 768cb4c..4365f9b 100644
--- a/legged_gym/envs/base/legged_robot_config.py
+++ b/legged_gym/envs/base/legged_robot_config.py
@@ -38,6 +38,7 @@ class LeggedRobotCfg(BaseConfig):
         terrain_proportions = [0.1, 0.1, 0.1, 0.2, 0.2, 0.1, 0.1, 0.1, 0.0]
         # trimesh only:
         slope_treshold = 0.75 # slopes above this threshold will be corrected to vertical surfaces
+        move_down_by_acuumulated_xy_command = False # move down the terrain curriculum based on accumulated xy command distance instead of absolute distance
 
     class commands:
         curriculum = False
@@ -53,6 +54,7 @@ class LeggedRobotCfg(BaseConfig):
         limit_vel_invert_when_continuous = True # invert the limit logic when using continuous sample limit velocity commands
         limit_vel = {"lin_vel_x": [-1, 1], "lin_vel_y": [-1, 1], "ang_vel_yaw": [-1, 0, 1]} # sample vel commands from min [-1] or zero [0] or max [1] range only
         stop_heading_at_limit = True # stop heading updates when vel is limited
+        dynamic_resample_commands = False # sample commands with low bounds
         command_range_curriculum = [] # list for command range curriculums at specific training iterations
         # eg: [{
         #     'iter': 20000, # training iteration at which the command ranges are updated
diff --git a/legged_gym/envs/go2/go2_config.py b/legged_gym/envs/go2/go2_config.py
index feee3d2..8546c20 100644
--- a/legged_gym/envs/go2/go2_config.py
+++ b/legged_gym/envs/go2/go2_config.py
@@ -93,6 +93,7 @@ class GO2Cfg(LeggedRobotCfg):
         # terrain_proportions = [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0]
         # terrain_proportions = [0.3, 0.3, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1]
         # terrain_proportions = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]
+        move_down_by_acuumulated_xy_command = True # move down the terrain curriculum based on accumulated xy command distance instead of absolute distance
         
     class commands(LeggedRobotCfg.commands):
         curriculum = False
@@ -107,6 +108,7 @@ class GO2Cfg(LeggedRobotCfg):
         limit_vel_invert_when_continuous = True # invert the limit logic when using continuous sample limit velocity commands
         limit_vel = {"lin_vel_x": [-1, 1], "lin_vel_y": [-1, 1], "ang_vel_yaw": [-1, 0, 1]} # sample vel commands from min [-1] or zero [0] or max [1] range only
         stop_heading_at_limit = True # stop heading updates when vel is limited
+        dynamic_resample_commands = True # sample commands with low bounds
         command_range_curriculum = [{ # list for command range curriculums at specific training iterations
             'iter': 20000, # training iteration at which the command ranges are updated
             'lin_vel_x': [-1.0, 1.0], # min max [m/s]
diff --git a/legged_gym/envs/go2/go2_config_vanilla.py b/legged_gym/envs/go2/go2_config_vanilla.py
new file mode 100644
index 0000000..ff37c27
--- /dev/null
+++ b/legged_gym/envs/go2/go2_config_vanilla.py
@@ -0,0 +1,313 @@
+# Don't forget to change IS_HARD = False, if you want to use original training setting
+import math
+from legged_gym.envs.base.legged_robot_config import LeggedRobotCfg, LeggedRobotCfgPPO, LeggedRobotCfgCTS, LeggedRobotCfgMoECTS, LeggedRobotCfgMoECTS, LeggedRobotCfgMCPCTS, LeggedRobotCfgACMoECTS, LeggedRobotCfgDualMoECTS, LeggedRobotCfgREMCTS
+
+class GO2Cfg(LeggedRobotCfg):
+    class init_state(LeggedRobotCfg.init_state):
+        pos = [0.0, 0.0, 0.42] # x,y,z [m]
+        default_joint_angles = { # = target angles [rad] when action = 0.0
+            'FL_hip_joint': 0.1,   # [rad]
+            'RL_hip_joint': 0.1,   # [rad]
+            'FR_hip_joint': -0.1 ,  # [rad]
+            'RR_hip_joint': -0.1,   # [rad]
+
+            'FL_thigh_joint': 0.8,     # [rad]
+            'RL_thigh_joint': 1.,   # [rad]
+            'FR_thigh_joint': 0.8,     # [rad]
+            'RR_thigh_joint': 1.,   # [rad]
+
+            'FL_calf_joint': -1.5,   # [rad]
+            'RL_calf_joint': -1.5,    # [rad]
+            'FR_calf_joint': -1.5,  # [rad]
+            'RR_calf_joint': -1.5,    # [rad]
+        }
+        turn_over = False # initialize the robot in a flipped over position
+        # turn_over_proportions = [0.1, 0.3, 0.6] # proportions for backflip, sideflip, noflip
+        turn_over_proportions = [0.0, 0.2, 0.8] # proportions for backflip, sideflip, noflip
+        turn_over_init_heights = { # initial heights range for each flip type
+            'backflip': [0.10, 0.15],
+            'sideflip': [0.16, 0.21],
+        }
+        # turn_over_proportions = [0.0, 1.0, 0.0] # proportions for backflip, sideflip, noflip
+
+    class env(LeggedRobotCfg.env):
+        num_envs = 8192
+        num_observations = 45
+        # obs(45) + base_lin_vel(3) + height_measurements(187)
+        num_privileged_obs = 45 + 3 + 4 + 12 + 12 + 187  # 263
+        # num_privileged_obs = 45 + 3 + 187  # 235
+        # num_privileged_obs = 48  # without height measurements
+        episode_length_s = 25
+
+    class domain_rand(LeggedRobotCfg.domain_rand):
+        ### Robot properties ###
+        randomize_friction = True
+        friction_range = [0.0, 2.0]
+
+        randomize_base_mass = True
+        added_mass_range = [-1., 1.]
+
+        randomize_link_mass = True
+        multiplied_link_mass_range = [0.9, 1.1]
+
+        randomize_base_com = True
+        added_base_com_range = [-0.03, 0.03]
+
+        randomize_restitution = True # restitution to robot links (Robot init)
+        restitution_range = [0.0, 0.5]
+
+        ### Environment reset ###
+        randomize_pd_gains = True
+        stiffness_multiplier_range = [0.9, 1.1]  
+        damping_multiplier_range = [0.9, 1.1]    
+
+        randomize_motor_zero_offset = True
+        motor_zero_offset_range = [-0.035, 0.035]
+
+        randomize_motor_strength = True # (Env reset)
+        motor_strength_range = [0.8, 1.2]
+
+        ### Environment step ###
+        push_robots = True
+        push_interval_s = 4
+        max_push_vel_xy = 0.4
+        max_push_ang_vel = 0.6
+
+        randomize_action_delay = True # use last_action with 0~20 ms delay, 4 decimation
+
+    class control(LeggedRobotCfg.control):
+        # PD Drive parameters:
+        control_type = 'P'
+        stiffness = {'joint': 20.0}  # [N*m/rad]
+        damping = {'joint': 0.5}     # [N*m*s/rad]
+        # action scale: target angle = actionScale * action + defaultAngle
+        action_scale = 0.25
+        # decimation: Number of control action updates @ sim DT per policy DT
+        decimation = 4
+    
+    class terrain(LeggedRobotCfg.terrain):
+        max_init_terrain_level = 5
+        # [wave, slope, rough_slope, stairs up, stairs down, obstacles, stepping_stones, gap, flat]
+        # terrain_proportions = [0.2, 0.05, 0.05, 0.30, 0.05, 0.25, 0.0, 0.0, 0.1]  # 更偏向wave
+        terrain_proportions = [0.05, 0.20, 0.05, 0.25, 0.10, 0.20, 0.0, 0.0, 0.15]  # 这个更偏向平地斜坡
+        # terrain_proportions = [0.20, 0.05, 0.05, 0.30, 0.15, 0.20, 0.0, 0.0, 0.05]  # 更偏向wave和stairs
+        # terrain_proportions = [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0]
+        # terrain_proportions = [0.3, 0.3, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1]
+        # terrain_proportions = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]
+        move_down_by_acuumulated_xy_command = False # move down the terrain curriculum based on accumulated xy command distance instead of absolute distance
+        
+    class commands(LeggedRobotCfg.commands):
+        curriculum = False
+        max_curriculum = 1.
+        num_commands = 4 # default: lin_vel_x, lin_vel_y, ang_vel_yaw (in heading mode ang_vel_yaw is recomputed from heading error)
+        resampling_time = 5. # time before command are changed[s]
+        heading_command = False # if true: compute ang vel command from heading error
+        # start training with zero commands and then gradually increase zero command probability
+        zero_command_curriculum = None
+        # zero_command_curriculum = {'start_iter': 0, 'end_iter': 1500, 'start_value': 0.0, 'end_value': 0.1}
+        limit_ang_vel_at_zero_command_prob = 0.0 # probability of add limiting angular velocity commands when zero command is sampled
+        limit_vel_prob = 0.0 # probability of limiting linear velocity command
+        limit_vel_invert_when_continuous = True # invert the limit logic when using continuous sample limit velocity commands
+        limit_vel = {"lin_vel_x": [-1, 1], "lin_vel_y": [-1, 1], "ang_vel_yaw": [-1, 0, 1]} # sample vel commands from min [-1] or zero [0] or max [1] range only
+        stop_heading_at_limit = True # stop heading updates when vel is limited
+        dynamic_resample_commands = False # sample commands with low bounds
+        command_range_curriculum = []
+        # command_range_curriculum = [{ # list for command range curriculums at specific training iterations
+        #     'iter': 20000, # training iteration at which the command ranges are updated
+        #     'lin_vel_x': [-1.0, 1.0], # min max [m/s]
+        #     'lin_vel_y': [-1.0, 1.0], # min max [m/s]
+        #     'ang_vel_yaw': [-1.5, 1.5], # min max [rad/s]
+        #     'heading': [-1.57, 1.57], # min max [rad]
+        # }, { # list for command range curriculums at specific training iterations
+        #     'iter': 50000, # training iteration at which the command ranges are updated
+        #     'lin_vel_x': [-2.0, 2.0], # min max [m/s]
+        #     'lin_vel_y': [-1.0, 1.0], # min max [m/s]
+        #     'ang_vel_yaw': [-2.0, 2.0], # min max [rad/s]
+        #     'heading': [-1.57, 1.57], # min max [rad]
+        # }]
+        turn_over_zero_time = { # if turn_over is true, time robot must be stable before sampling new commands after a turn over
+            "backflip": 5.0,
+            "sideflip": 3.0,
+        }
+        # [wave, slope, rough slope, stairs up, stairs down, obstacles, stepping stones, gap, flat]
+        terrain_max_command_ranges = [
+            {'lin_vel_x': [-1.5, 1.5], 'lin_vel_y': [-1.0, 1.0], 'ang_vel_yaw': [-1.5, 1.5], 'heading': [-1.57, 1.57]},  # wave
+            {'lin_vel_x': [-1.5, 1.5], 'lin_vel_y': [-1.0, 1.0], 'ang_vel_yaw': [-1.5, 1.5], 'heading': [-1.57, 1.57]},  # slope
+            {'lin_vel_x': [-1.5, 1.5], 'lin_vel_y': [-1.0, 1.0], 'ang_vel_yaw': [-1.5, 1.5], 'heading': [-1.57, 1.57]},  # rough slope
+            {'lin_vel_x': [-1.0, 1.0], 'lin_vel_y': [-1.0, 1.0], 'ang_vel_yaw': [-1.5, 1.5], 'heading': [-1.57, 1.57]},  # stairs up
+            {'lin_vel_x': [-1.0, 1.0], 'lin_vel_y': [-1.0, 1.0], 'ang_vel_yaw': [-1.5, 1.5], 'heading': [-1.57, 1.57]},  # stairs down
+            {'lin_vel_x': [-1.0, 1.0], 'lin_vel_y': [-1.0, 1.0], 'ang_vel_yaw': [-1.5, 1.5], 'heading': [-1.57, 1.57]},  # obstacles
+            {'lin_vel_x': [-1.0, 1.0], 'lin_vel_y': [-1.0, 1.0], 'ang_vel_yaw': [-1.5, 1.5], 'heading': [-1.57, 1.57]},  # stepping stones
+            {'lin_vel_x': [-1.0, 1.0], 'lin_vel_y': [-1.0, 1.0], 'ang_vel_yaw': [-1.5, 1.5], 'heading': [-1.57, 1.57]},  # gap
+            {'lin_vel_x': [-2.0, 2.0], 'lin_vel_y': [-1.0, 1.0], 'ang_vel_yaw': [-2.0, 2.0], 'heading': [-1.57, 1.57]},  # flat
+        ]
+
+        class ranges:
+            lin_vel_x = [-2.0, 2.0] # min max [m/s]
+            lin_vel_y = [-1.0, 1.0] # min max [m/s]
+            ang_vel_yaw = [-2.0, 2.0]   # min max [rad/s]
+            heading = [-1.57, 1.57] # min max [rad]
+        
+    class asset(LeggedRobotCfg.asset):
+        file = '{LEGGED_GYM_ROOT_DIR}/resources/robots/go2/urdf/go2.urdf'
+        name = "go2"
+        foot_name = "foot"
+        penalize_contacts_on = ["thigh", "calf"]
+        terminate_after_contacts_on = ["base"]
+        self_collisions = 1 # 1 to disable, 0 to enable...bitwise filter
+  
+    class rewards(LeggedRobotCfg.rewards):
+        soft_dof_pos_limit = 0.9
+        base_height_target = 0.38
+        only_positive_rewards = False
+        max_contact_force = 147. # forces above this value are penalized, go2 weight 15kg
+        curriculum_rewards = [
+            {'reward_name': 'lin_vel_z', 'start_iter': 0, 'end_iter': 1500, 'start_value': 1.0, 'end_value': 0.0},
+            {'reward_name': 'correct_base_height', 'start_iter': 0, 'end_iter': 5000, 'start_value': 1.0, 'end_value': 10.0},
+            # {'reward_name': 'dof_power', 'start_iter': 0, 'end_iter': 3000, 'start_value': 1.0, 'end_value': 0.1},
+            # {'reward_name': 'upright', 'start_iter': 0, 'end_iter': 1500, 'start_value': 1.0, 'end_value': 0.0},
+        ]
+        tracking_sigma = 0.25 # tracking reward = exp(-error^2/sigma)
+        dynamic_sigma = None
+        # dynamic_sigma = { # linear interpolation of sigma based on command velocity, **Must start terrain curriculum first**
+        #     "min_lin_vel": 0.5, # min abs linear velocity to have default sigma
+        #     "max_lin_vel": 1.5, # max abs linear velocity to have max sigma
+        #     "min_ang_vel": 1.0, # min abs angular velocity to have default sigma
+        #     "max_ang_vel": 2.0, # max abs angular velocity to have max sigma
+        #     # wave, slope, rough_slope, stairs up, stairs down, obstacles, stepping_stones, gap, flat]
+        #     # "max_sigma": [1/3, 1/4, 1/4, 1/2.7, 1/2.7, 1/2, 1, 1, 1/4]
+        #     "max_sigma": [5/12, 1/4, 1/4, 1/2, 1/2, 3/4, 1, 1, 1/4]
+        # }
+        min_legs_distance = 0.1  # min distance between legs to not be considered stumbling
+        class scales:
+            # tracking_lin_vel = 1.0
+            # tracking_ang_vel = 0.2
+            # lin_vel_z = -10.0
+            # base_height = -50.0
+            # action_rate = -0.005
+            # similar_to_default = -0.1
+            # dof_power = -1e-3  # 能够明显抑制跳跃
+            # dof_acc = -3e-7
+
+            # tracking_lin_vel = 1.0
+            # tracking_ang_vel = 0.5
+            # lin_vel_z = -2.0
+            # ang_vel_xy = -0.05
+            # dof_acc = -2.5e-7
+            # dof_power = -1e-3  # 能够明显抑制跳跃
+            # # torques = -1e-4  # 无用会走着走着倒了
+            # correct_base_height = -10.0
+            # action_rate = -0.01
+            # action_smoothness = -0.01
+            # collision = -1.0
+            # dof_pos_limits = -2.0
+            # feet_regulation = -0.05
+            # hip_to_default = -0.1
+            # similar_to_default = -0.05
+
+            # CTS reward
+            tracking_lin_vel = 1.0
+            tracking_ang_vel = 0.5
+            lin_vel_z = -2.0
+            ang_vel_xy = -0.05
+            dof_acc = -2.5e-7
+            dof_power = -2e-5
+            torques = -1e-4
+            correct_base_height = -1.0
+            action_rate = -0.01
+            action_smoothness = -0.01
+            collision = -1.0
+            dof_pos_limits = -2.0
+            feet_regulation = -0.05
+            # CTS奖励训出来双脚距离非常近, 真机效果很差, 但是sim2sim能上20cm楼梯, 尝试加入hip_to_default奖励或similar_to_default奖励
+            hip_to_default = -0.05  # 在训练到y=1.5时, 双脚会明显碰撞, 为避免该问题提升hip, 效果更差, 还是保持0.05 (y最大也只到0.1了)
+            # legs_distance = -1.5  # 奖励双脚距离, 避免CTS训练出来双脚距离过近, 尝试加入后robogauge flat验证效果变差, 删除
+            # similar_to_default = -0.01
+            # feet_contact_forces = -1.0  # 尝试加入但并没有起到任何效果, 删除
+
+        turn_over_roll_threshold = math.pi / 4 # threshold on roll to use turn over rewards
+        class turn_over_scales:
+            upright = 1.0
+            # dof_acc = -2.5e-7
+            # dof_power = -2e-5
+            # action_rate = -0.001
+            # action_smoothness = -0.001
+
+    class noise(LeggedRobotCfg.noise):
+        add_noise = True
+
+class GO2CfgPPO(LeggedRobotCfgPPO):
+    class algorithm(LeggedRobotCfgPPO.algorithm):
+        entropy_coef = 0.01
+    class runner(LeggedRobotCfgPPO.runner):
+        run_name = ''
+        experiment_name = 'go2_ppo'
+        max_iterations = 100000
+        save_interval = 500
+
+class GO2CfgCTS(LeggedRobotCfgCTS):
+    class runner(LeggedRobotCfgCTS.runner):
+        num_steps_per_env = 24
+        run_name = ''
+        experiment_name = 'go2_cts'
+        max_iterations = 150000
+        save_interval = 500
+    
+    class policy(LeggedRobotCfgCTS.policy):
+        latent_dim = 32
+        norm_type = 'l2norm'
+
+class GO2CfgMoECTS(LeggedRobotCfgMoECTS):
+    class policy(LeggedRobotCfgMoECTS.policy):
+        obs_no_goal_mask = [True] * 6 + [False] * 3 + [True] * 36  # mask for obs without command info
+        student_expert_num = 8 # number of experts in the student model
+    
+    class algorithm(LeggedRobotCfgMoECTS.algorithm):
+        load_balance_coef = 0.01
+
+    class runner(LeggedRobotCfgMoECTS.runner):
+        run_name = ''
+        experiment_name = 'go2_moe_cts'
+        max_iterations = 150000
+        save_interval = 500
+
+class GO2CfgMCPCTS(LeggedRobotCfgMCPCTS):
+    class policy(LeggedRobotCfgMCPCTS.policy):
+        obs_no_goal_mask = [True] * 6 + [False] * 3 + [True] * 36  # mask for obs without command info
+        student_expert_num = 8 # number of experts in the student model
+
+    class runner(LeggedRobotCfgMCPCTS.runner):
+        run_name = ''
+        experiment_name = 'go2_mcp_cts'
+        max_iterations = 150000
+        save_interval = 500
+
+class GO2CfgACMoECTS(LeggedRobotCfgACMoECTS):
+    class policy(LeggedRobotCfgACMoECTS.policy):
+        expert_num = 8  # number of experts in the student model
+    
+    class runner(LeggedRobotCfgACMoECTS.runner):
+        run_name = ''
+        experiment_name = 'go2_ac_moe_cts'
+        max_iterations = 150000
+        save_interval = 500
+
+class GO2CfgDualMoECTS(LeggedRobotCfgDualMoECTS):
+    class policy(LeggedRobotCfgDualMoECTS.policy):
+        expert_num = 8  # number of experts in the student model
+    
+    class runner(LeggedRobotCfgDualMoECTS.runner):
+        run_name = ''
+        experiment_name = 'go2_dual_moe_cts'
+        max_iterations = 150000
+        save_interval = 500
+
+class GO2CfgREMCTS(LeggedRobotCfgREMCTS):
+    class policy(LeggedRobotCfgREMCTS.policy):
+        expert_num = 8  # number of experts in the student model
+    
+    class runner(LeggedRobotCfgREMCTS.runner):
+        run_name = ''
+        experiment_name = 'go2_rem_cts'
+        max_iterations = 150000
+        save_interval = 500