diff --git a/lerobot/scripts/server/policy_server.py b/lerobot/scripts/server/policy_server.py
index b2eb00fa..841273bf 100644
--- a/lerobot/scripts/server/policy_server.py
+++ b/lerobot/scripts/server/policy_server.py
@@ -17,15 +17,11 @@ def get_device():
 
 class PolicyServer(async_inference_pb2_grpc.AsyncInferenceServicer):
     def __init__(self, policy: PreTrainedPolicy = None):        
+        # TODO: Add code for loading and using policy for inference
         self.policy = policy
+        # TODO: Add device specification for policy inference at init
+        self.observation = None
 
-        # TODO: Add device specification for policy inference
-        # self.observation = None
-        self.observation = async_inference_pb2.Observation(
-            transfer_state=2,
-            data=np.array([1], dtype=np.float32).tobytes()
-        )
-            
         self.lock = threading.Lock()
         # keeping a list of all observations received from the robot client
         self.observations = []
@@ -62,7 +58,7 @@ class PolicyServer(async_inference_pb2_grpc.AsyncInferenceServicer):
     def StreamActions(self, request, context):
         """Stream actions to the robot client"""
         client_id = context.peer()
-        print(f"Client {client_id} connected for action streaming")
+        # print(f"Client {client_id} connected for action streaming")
         
         with self.lock:
             yield self._generate_and_queue_action(self.observation)
@@ -80,17 +76,20 @@ class PolicyServer(async_inference_pb2_grpc.AsyncInferenceServicer):
         raise NotImplementedError("Not implemented")
 
     def _generate_and_queue_action(self, observation):
-        """Generate an action based on the observation (dummy logic).
+        """Generate a buffer of actions based on the observation (dummy logic).
         Mainly used for testing purposes"""
-        # Debinarize the observation data
+        time.sleep(2)
+        # Debinarize observation data
         data = np.frombuffer(
             observation.data, 
             dtype=np.float32
         )
         # dummy transform on the observation data
-        action = (data * 1.4).sum()
-        # map action to bytes
-        action_data = np.array([action], dtype=np.float32).tobytes()
+        action_content = (data * 2).sum().item()
+        action_data = (action_content * np.ones(
+            shape=(10, 5),  # 10 5-dimensional actions
+            dtype=np.float32
+        )).tobytes()
 
         action = async_inference_pb2.Action(
             transfer_state=observation.transfer_state,