diff --git a/roboimi/demos/config.yaml b/roboimi/demos/config.yaml
index 3b16eb1..efb6f1c 100644
--- a/roboimi/demos/config.yaml
+++ b/roboimi/demos/config.yaml
@@ -38,6 +38,11 @@ episode_len:      # leave empty here by default
 camera_names: []  # leave empty here by default
 xml_dir:          # leave empty here by default
 
+# action smoothing settings (for GR00T)
+use_action_smoothing: true
+smooth_method: "ema"     # Options: "ema", "moving_avg", "lowpass", "none"
+smooth_alpha: 0.3        # Smoothing factor (0-1), smaller = smoother
+
 # transformer settings
 batch_size: 15                          
 state_dim: 16            
diff --git a/roboimi/demos/diana_eval.py b/roboimi/demos/diana_eval.py
index a5e71e5..e6994d4 100644
--- a/roboimi/demos/diana_eval.py
+++ b/roboimi/demos/diana_eval.py
@@ -12,6 +12,71 @@ from roboimi.envs.double_pos_ctrl_env import make_sim_env
 from roboimi.utils.act_ex_utils import sample_transfer_pose
 
 
+class ActionSmoother:
+    """
+    动作平滑器，支持多种平滑策略
+    """
+    def __init__(self, action_dim, method='ema', alpha=0.3, window_size=5):
+        """
+        Args:
+            action_dim: 动作维度
+            method: 平滑方法 ('ema', 'moving_avg', 'lowpass', 'none')
+            alpha: EMA 平滑系数 (0-1)，越小越平滑
+            window_size: 滑动窗口大小
+        """
+        self.action_dim = action_dim
+        self.method = method
+        self.alpha = alpha
+        self.window_size = window_size
+        self.history = []
+        self.prev_action = None
+
+    def smooth(self, action):
+        """
+        对动作进行平滑处理
+
+        Args:
+            action: 当前动作 [action_dim]
+
+        Returns:
+            smoothed_action: 平滑后的动作
+        """
+        if self.method == 'none':
+            return action
+
+        if self.method == 'ema':
+            # 指数移动平均
+            if self.prev_action is None:
+                smoothed = action
+            else:
+                smoothed = self.alpha * action + (1 - self.alpha) * self.prev_action
+            self.prev_action = smoothed
+            return smoothed
+
+        elif self.method == 'moving_avg':
+            # 滑动平均
+            self.history.append(action.copy())
+            if len(self.history) > self.window_size:
+                self.history.pop(0)
+            return np.mean(self.history, axis=0)
+
+        elif self.method == 'lowpass':
+            # 一阶低通滤波器
+            if self.prev_action is None:
+                smoothed = action
+            else:
+                smoothed = self.prev_action + self.alpha * (action - self.prev_action)
+            self.prev_action = smoothed
+            return smoothed
+
+        else:
+            raise ValueError(f"Unknown smoothing method: {self.method}")
+
+    def reset(self):
+        """重置平滑器状态"""
+        self.history = []
+        self.prev_action = None
+
 
 #should be added into IOUtils
 def get_image(obs,camera_names):
@@ -57,6 +122,19 @@ def run_episode(config, policy, stats, save_episode,num_rollouts):
     pre_process = lambda s_qpos: (s_qpos - stats['qpos_mean']) / stats['qpos_std']
     post_process = lambda a: a * stats['action_std'] + stats['action_mean']
     box_pos = sample_transfer_pose()
+
+    # 初始化动作平滑器
+    action_dim = config['action_dim']
+    use_smoothing = config.get('use_action_smoothing', False)
+    smooth_method = config.get('smooth_method', 'ema')
+    smooth_alpha = config.get('smooth_alpha', 0.3)
+
+    if use_smoothing and config['policy_class'] == "GR00T":
+        smoother = ActionSmoother(action_dim, method=smooth_method, alpha=smooth_alpha)
+        print(f"Action smoothing enabled: method={smooth_method}, alpha={smooth_alpha}")
+    else:
+        smoother = None
+
     for rollout_id in range(num_rollouts):
         print("\nrollout_id===",rollout_id,"\n")
         image_list = []
@@ -64,6 +142,11 @@ def run_episode(config, policy, stats, save_episode,num_rollouts):
         query_frequency = config['policy_config'].get('num_queries', 1)
         print("query_freq =====",query_frequency)
         env.reset(box_pos)
+
+        # 重置平滑器
+        if smoother is not None:
+            smoother.reset()
+
         with torch.inference_mode():
             for t in range(700):
                 image_list.append(env._get_image_obs()['images'] if 'images' in env._get_image_obs() else {print("img error")})
@@ -83,6 +166,11 @@ def run_episode(config, policy, stats, save_episode,num_rollouts):
 
 
                 action = post_process(raw_action)
+
+                # 应用动作平滑（仅对 GR00T）
+                if smoother is not None:
+                    action = smoother.smooth(action)
+
                 print("action == ",action)
                 env.step_jnt(action)
                 rewards.append(env.rew)