continue with optimizer node

requirements.txt

@@ -9,7 +9,4 @@ scikit-learn~=1.4.0
 transitions==0.9.0
 movement-primitives[all]~=0.7.0
 cma~=3.3.0
-rclpy~=3.3.11
+PyYAML~=5.4.1
-PyYAML~=5.4.1
-rpyutils~=0.2.1
-turtlesim~=1.4.2

src/interaction_msgs/CMakeLists.txt

@@ -12,6 +12,8 @@ rosidl_generate_interfaces(${PROJECT_NAME}
     "srv/Query.srv"
     "srv/Task.srv"
     "srv/TaskEvaluation.srv"
+    "srv/UserInterface.srv"
+    "srv/ParameterChange.srv"
     "msg/OptimizerState.msg"
     "msg/Opt2UI.msg"
     "msg/UI2Opt.msg"

src/interaction_msgs/srv/ParameterChange.srv

@@ -0,0 +1,6 @@
+string[] parameter_name
+string[] parameter_type  # One of 'float', 'string', 'bool', potentially others
+string[] parameter_value
+---
+bool success
+string message # For error reporting

src/interaction_msgs/srv/TaskEvaluation.srv

@@ -1,10 +1,10 @@
 bool user_input
 # case if user_input is true
 uint16 nr_of_population
-float32[] user_mean # Length: number_of_dimensions * number_of_parameters_per_dimension
+float32[] user_parameters # Length: number_of_dimensions * number_of_parameters_per_dimension
-float32[] user_covariance_diag # same as user_mean
+float32[] user_covariance_diag # Length: number_of_dimensions * number_of_parameters_per_dimension
 float32[] current_cma_mean # Length: number_of_dimensions * number_of_parameters_per_dimension
-float32[] conditional_points # Length: (number_of_dimensions + time_stamp) * number_of_conditional_points
+float32[] conditional_points # Length: (number_of_dimensions + time_stamp[0,1]) * number_of_conditional_points
 float32 weight_parameter # this parameter sets the weighted average 0 dont trust user 1 completly trust user (it is set by the user or it is decays over time i have to do some experiments on that)
 
 # case if user_input is false
@@ -12,7 +12,6 @@ uint16 number_of_population
 uint16 number_of_dimensions # this is the number of ProMPs * 2 (Position and Velocity)
 uint16 number_of_parameters_per_dimensions
 float32[] parameter_array # Length: number_of_population * number_of_dimensions * number_of_parameters_per_dimension
-
 ---
 # response
 float32[] parameter_array  # this is needed because in case of user input the parameters arent known yet

src/interaction_msgs/srv/UserInterface.srv

@@ -0,0 +1,8 @@
+float32[] current_cma_mean # Length: number_of_dimensions * number_of_parameters_per_dimension
+float32[] best_parameters # Length: number_of_dimensions * number_of_parameters_per_dimension
+float32[] current_user_covariance_diag # Length: number_of_dimensions * number_of_parameters_per_dimension
+---
+float32[] user_parameters # Length: number_of_dimensions * number_of_parameters_per_dimension
+float32[] user_covariance_diag # Length: number_of_dimensions * number_of_parameters_per_dimension
+float32[] conditional_points # Length: (number_of_dimensions + time_stamp[0,1]) * number_of_conditional_points
+float32 weight_parameter # this parameter sets the weighted average 0 dont trust user 1 completly trust user (it is set by the user or it is decays over time i have to do some experiments on that)

src/interaction_optimizers/interaction_optimizers/cmaes_optimization_node.py

@@ -2,6 +2,7 @@ import os
 
 import rclpy
 from rclpy.node import Node
+from rclpy.parameter import Parameter
 
 from transitions import Machine
 import cma
@@ -12,6 +13,8 @@ from src.interaction_utils.serialization import flatten_population, unflatten_po
 # Msg/Srv/Action
 from interaction_msgs.srv import Query
 from interaction_msgs.srv import TaskEvaluation
+from interaction_msgs.srv import ParameterChange
+from interaction_msgs.srv import UserInterface
 from std_msgs.msg import Bool
 
 
@@ -21,6 +24,8 @@ class CMAESOptimizationNode(Node):
 
         # CMA-ES Attributes
         self.cmaes = None
+        self.number_of_initial_episodes = self.declare_parameter('number_of_initial_episodes', 5)
+        self.episode = 0
         self.number_of_dimensions = 3
         self.number_of_parameters_per_dimensions = 10
         # the number of weights is double the number of dims * params per dim since its Position and Velocity
@@ -31,7 +36,8 @@ class CMAESOptimizationNode(Node):
         # Query Attributes
         self.query_metric = 'random'
         self.query_parameters = {}
-        self.best_rewards_per_iteration = []
+        self.best_parameters_per_interation = []
+        self.best_reward_per_iteration = []
         # ROS2 Interfaces
         self.__future = None
         self.__task_response = None
@@ -48,6 +54,7 @@ class CMAESOptimizationNode(Node):
         # Topics
 
         # Service
+        self.parameter_srv = self.create_service(ParameterChange, 'cmaes_parameter_srv', self.parameter_callback)
         self.query_srv = self.create_client(Query, 'query_srv')
         self.task_srv = self.create_client(TaskEvaluation, 'task_srv')
 
@@ -125,6 +132,7 @@ class CMAESOptimizationNode(Node):
         elif self.query_metric == 'improvement':
             pass
 
+        self.query_srv.wait_for_service(10)
         self.__future = self.query_srv.call_async(request)
         self.__future.add_done_callback(self.handle_query_response)
 
@@ -149,15 +157,41 @@ class CMAESOptimizationNode(Node):
         self.data_send_for_evaluation()
 
     def on_enter_interactive_mode(self):
-        pass
+        request = UserInterface.Request()
+        request.current_cma_mean = flatten_population(self.cmaes.mean)
+        request.best_parameters
 
     def on_enter_prepare_user_data_for_evaluation(self):
         pass
 
     def on_enter_update_optimizer(self):
-        pass
+        population = unflatten_population(self.__task_response.parameter_array,
+                                          self.cmaes.popsize,
+                                          self.number_of_dimensions,
+                                          self.number_of_parameters_per_dimensions)
+        scores = self.__task_response.scores
+
+        self.cmaes.tell(population, scores)
+
+        # save best results
+        best_idx = max(enumerate(scores), key=lambda item: item[1])[0]
+        self.best_reward_per_iteration.append(scores[best_idx].copy())
+        self.best_parameters_per_interation.append(population[best_idx, :, :].copy())
+
+        self.__task_response = None
+        self.episode += 1
+
+        self.optimizer_updated()
 
     def on_enter_check_termination(self):
+        max_episodes = self.get_parameter('max_episode_count').get_parameter_value().integer_value
+
+        if self.episode >= max_episodes:
+            self.finished()
+        else:
+            self.next_optimizer_step()
+
+    def on_enter_complete(self):
         pass
 
     def on_enter_error_recovery(self):
@@ -166,6 +200,59 @@ class CMAESOptimizationNode(Node):
     # endregion
 
     # region Callback Functions
+    def parameter_callback(self, request, response):
+        param_names = request.parameter_name
+        param_types = request.parameter_type
+        param_values_str = request.parameter_value
+
+        if len(param_names) != len(param_types) or len(param_types) != len(param_values_str):
+            response.success = False
+            response.message = 'Lists must have the same size'
+            return response
+
+        try:
+            # Parameter update loop
+            all_params = []
+            for i in range(len(param_names)):
+                param_name = param_names[i]
+                param_type = param_types[i]
+                param_value_str = param_values_str[i]
+
+                # Input Validation (adjust as needed)
+                if param_type not in ['float32', 'string', 'bool']:
+                    response.success = False
+                    response.message = 'Unsupported parameter type'
+                    return response
+
+                    # Value Conversion (based on param_type)
+                if param_type == 'float32':
+                    param_value = float(param_value_str)
+                elif param_type == 'bool':
+                    param_value = (param_value_str.lower() == 'true')
+                else:  # 'string'
+                    param_value = param_value_str
+
+                # Compose the all parameter list
+                param = Parameter(param_name, Parameter.Type.from_parameter_value(param_value), param_value)
+                all_params.append(param)
+
+            # Attempt to set the parameter
+            set_params_result = self.set_parameters(all_params)
+
+            response.success = True  # Assume success unless set_parameters fails
+            for result in set_params_result:
+                if not result.successful:
+                    response.success = False
+                    response.message = result.reason
+                    break
+            return response
+
+        except Exception as e:
+            self.get_logger().error(f'Parameter update failed: {str(e)}')
+            response.success = False
+            response.message = 'Parameter update failed, please check logs'
+            return response
+
     def check_heartbeats(self):
         if self.last_mr_heartbeat_time:
             current_time = self.get_clock().now().nanoseconds
@@ -190,6 +277,9 @@ class CMAESOptimizationNode(Node):
         try:
             response = future.result()
 
+            if self.episode < self.number_of_initial_episodes:
+                self.non_interaction()
+
             if response.interaction:
                 self.interaction()
             else:
@@ -208,4 +298,4 @@ class CMAESOptimizationNode(Node):
             self.get_logger().error(f'Task service call failed: {e}')
             self.error_trigger()
 
-    # endregion
+    # endregion