diff --git a/embodichain/toolkits/graspkit/pg_grasp/antipodal_generator.py b/embodichain/toolkits/graspkit/pg_grasp/antipodal_generator.py
index f6389ff8..658f4f88 100644
--- a/embodichain/toolkits/graspkit/pg_grasp/antipodal_generator.py
+++ b/embodichain/toolkits/graspkit/pg_grasp/antipodal_generator.py
@@ -583,7 +583,7 @@ def get_grasp_poses(
         approach_direction: torch.Tensor,
         visualize_collision: bool = False,
         visualize_pose: bool = False,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
+    ) -> tuple[bool, torch.Tensor, float]:
         """Get grasp pose given approach direction.
 
         Uses the antipodal point pairs stored in ``self._hit_point_pairs``
@@ -603,19 +603,20 @@ def get_grasp_poses(
                 after computation.
 
         Returns:
-            A tuple ``(best_grasp_pose, best_open_length)`` where
-            ``best_grasp_pose`` is a ``(4, 4)`` homogeneous matrix and
-            ``best_open_length`` is a scalar.
+            is_success (bool): Whether a valid grasp pose is found.
+            best_grasp_pose (torch.Tensor): If a valid grasp pose is found, a tensor of shape (4, 4) representing the homogeneous transformation matrix of the best grasp pose in the world frame. Otherwise, an identity matrix.
+            best_open_length (float): If a valid grasp pose is found, a scalar representing the optimal gripper opening length. Otherwise, a zero tensor.
 
         Raises:
             RuntimeError: If :meth:`generate` or :meth:`annotate` has not
                 been called yet.
         """
         if self._hit_point_pairs is None:
-            raise RuntimeError(
+            logger.log_warning(
                 "No antipodal point pairs available. "
                 "Call generate() or annotate() first."
             )
+            return False, torch.eye(4, device=self.device), 0.0
         origin_points = self._hit_point_pairs[:, 0, :]
         hit_points = self._hit_point_pairs[:, 1, :]
         origin_points_ = self._apply_transform(origin_points, object_pose)
@@ -632,6 +633,10 @@ def get_grasp_poses(
         valid_mask = (
             positive_angle - torch.pi / 2
         ).abs() <= self.cfg.max_deviation_angle
+        if valid_mask.sum() == 0:
+            logger.log_warning("No valid antipodal pairs after angle filtering.")
+            return False, torch.eye(4, device=self.device), 0.0
+
         valid_grasp_x = grasp_x[valid_mask]
         valid_centers = centers[valid_mask]
 
@@ -650,6 +655,9 @@ def get_grasp_poses(
             is_visual=visualize_collision,
             collision_threshold=0.0,
         )
+        if is_colliding.logical_not().sum() == 0:
+            logger.log_warning("No valid antipodal pairs after angle filtering.")
+            return False, torch.eye(4, device=self.device), 0.0
         # get best grasp pose
         valid_grasp_poses = valid_grasp_poses[~is_colliding]
         valid_open_lengths = valid_open_lengths[~is_colliding]
@@ -674,7 +682,7 @@ def get_grasp_poses(
                 grasp_pose=best_grasp_pose,
                 open_length=best_open_length.item(),
             )
-        return best_grasp_pose, best_open_length
+        return True, best_grasp_pose, best_open_length
 
     @staticmethod
     def _grasp_pose_from_approach_direction(
diff --git a/scripts/tutorials/grasp/grasp_generator.py b/scripts/tutorials/grasp/grasp_generator.py
index bab09c03..16143215 100644
--- a/scripts/tutorials/grasp/grasp_generator.py
+++ b/scripts/tutorials/grasp/grasp_generator.py
@@ -271,11 +271,20 @@ def get_grasp_traj(sim: SimulationManager, robot: Robot, grasp_xpos: torch.Tenso
     )
     obj_poses = mug.get_local_pose(to_matrix=True)
     grasp_xpos_list = []
-    for obj_pose in obj_poses:
-        grasp_pose, _ = grasp_generator.get_grasp_poses(
+
+    rest_xpos = robot.compute_fk(
+        qpos=robot.get_qpos("arm"), name="arm", to_matrix=True
+    )[0]
+    for i, obj_pose in enumerate(obj_poses):
+        is_success, grasp_pose, open_length = grasp_generator.get_grasp_poses(
             obj_pose, approach_direction, visualize_pose=False
         )
-        grasp_xpos_list.append(grasp_pose.unsqueeze(0))
+        if is_success:
+            grasp_xpos_list.append(grasp_pose.unsqueeze(0))
+        else:
+            logger.log_warning(f"No valid grasp pose found for {i}-th object.")
+            grasp_xpos_list.append(rest_xpos.unsqueeze(0))
+
     grasp_xpos = torch.cat(grasp_xpos_list, dim=0)
     cost_time = time.time() - start_time
     logger.log_info(f"Get grasp pose cost time: {cost_time:.2f} seconds")