diff --git a/teleop/robot_control/robot_hand_unitree.py b/teleop/robot_control/robot_hand_unitree.py
index b6782a1..10e5811 100644
--- a/teleop/robot_control/robot_hand_unitree.py
+++ b/teleop/robot_control/robot_hand_unitree.py
@@ -329,7 +329,7 @@ class Dex1_1_Gripper_Controller:
                              dual_gripper_state_out = None, dual_gripper_action_out = None):
         self.running = True
         if self.simulation_mode:
-            DELTA_GRIPPER_CMD = 1.0
+            DELTA_GRIPPER_CMD = 0.18
         else:   
             DELTA_GRIPPER_CMD = 0.18     # The motor rotates 5.4 radians, the clamping jaw slide open 9 cm, so 0.6 rad <==> 1 cm, 0.18 rad <==> 3 mm
         THUMB_INDEX_DISTANCE_MIN = 5.0
@@ -369,19 +369,20 @@ class Dex1_1_Gripper_Controller:
                     left_gripper_value  = left_gripper_value_in.value
                 with right_gripper_value_in.get_lock():
                     right_gripper_value = right_gripper_value_in.value
-
+                # get current dual gripper motor state
+                dual_gripper_state = np.array([left_gripper_state_value.value, right_gripper_state_value.value])
+                
                 if left_gripper_value != 0.0 or right_gripper_value != 0.0: # if input data has been initialized.
                     # Linear mapping from [0, THUMB_INDEX_DISTANCE_MAX] to gripper action range
                     left_target_action  = np.interp(left_gripper_value, [THUMB_INDEX_DISTANCE_MIN, THUMB_INDEX_DISTANCE_MAX], [LEFT_MAPPED_MIN, LEFT_MAPPED_MAX])
                     right_target_action = np.interp(right_gripper_value, [THUMB_INDEX_DISTANCE_MIN, THUMB_INDEX_DISTANCE_MAX], [RIGHT_MAPPED_MIN, RIGHT_MAPPED_MAX])
-
-                # get current dual gripper motor state
-                dual_gripper_state = np.array([left_gripper_state_value.value, right_gripper_state_value.value])
-
                 # clip dual gripper action to avoid overflow
-                left_actual_action  = np.clip(left_target_action,  dual_gripper_state[0] - DELTA_GRIPPER_CMD, dual_gripper_state[0] + DELTA_GRIPPER_CMD) 
-                right_actual_action = np.clip(right_target_action, dual_gripper_state[1] - DELTA_GRIPPER_CMD, dual_gripper_state[1] + DELTA_GRIPPER_CMD)
-
+                if not self.simulation_mode:
+                    left_actual_action  = np.clip(left_target_action,  dual_gripper_state[0] - DELTA_GRIPPER_CMD, dual_gripper_state[0] + DELTA_GRIPPER_CMD) 
+                    right_actual_action = np.clip(right_target_action, dual_gripper_state[1] - DELTA_GRIPPER_CMD, dual_gripper_state[1] + DELTA_GRIPPER_CMD)
+                else:
+                    left_actual_action  = left_target_action
+                    right_actual_action = right_target_action
                 dual_gripper_action = np.array([left_actual_action, right_actual_action])
 
                 if self.smooth_filter:
diff --git a/teleop/teleop_hand_and_arm.py b/teleop/teleop_hand_and_arm.py
index 6e41391..4916a3b 100644
--- a/teleop/teleop_hand_and_arm.py
+++ b/teleop/teleop_hand_and_arm.py
@@ -212,6 +212,9 @@ if __name__ == '__main__':
 
             if not args.headless:
                 tv_resized_image = cv2.resize(tv_img_array, (tv_img_shape[1] // 2, tv_img_shape[0] // 2))
+                if args.sim:
+                    wrist_resized_image = cv2.resize(wrist_img_array, (wrist_img_shape[1] // 2, wrist_img_shape[0] // 2))
+                    tv_resized_image = np.concatenate([tv_resized_image, wrist_resized_image], axis=1)
                 cv2.imshow("record image", tv_resized_image)
                 key = cv2.waitKey(1) & 0xFF
                 if key == ord('q'):