Franka experiment complete + debugged

src/active_bo_ros/active_bo_ros/AcquisitionFunctions/PreferenceExpectedImprovement.py

@@ -3,34 +3,47 @@ from scipy.stats import norm, multivariate_normal
 
 
 class PreferenceExpectedImprovement:
-    def __init__(self, nr_dims, nr_samples, lower_bound, upper_bound, initial_variance, update_variance, seed=None):
+    def __init__(self, nr_dims, nr_samples, initial_variance, update_variance, lower_bound=None, upper_bound=None, seed=None,
+                 fixed_dims=None):
 
         self.nr_dims = nr_dims
         self.nr_samples = int(nr_samples)
-
-        self.lower_bound = lower_bound
-        self.upper_bound = upper_bound
-
         self.init_var = initial_variance
         self.update_var = update_variance
 
+        if lower_bound is None:
+            self.lower_bound = [-1.] * nr_dims
+        else:
+            self.lower_bound = lower_bound
+
+        if upper_bound is None:
+            self.upper_bound = [1.] * nr_dims
+        else:
+            self.upper_bound = upper_bound
+
         self.rng = np.random.default_rng(seed=seed)
 
         # initial proposal distribution
         self.proposal_mean = np.zeros((nr_dims, 1))
         self.proposal_cov = np.diag(np.ones((nr_dims,)) * self.init_var)
 
+        # fixed dimension for robot experiment
+        self.fixed_dims = fixed_dims
+
     def rejection_sampling(self):
         samples = np.empty((0, self.nr_dims))
         while samples.shape[0] < self.nr_samples:
             # sample from the multi variate gaussian distribution
             sample = np.zeros((1, self.nr_dims))
             for i in range(self.nr_dims):
-                check = False
+                if i in self.fixed_dims:
-                while not check:
+                    sample[0, i] = self.fixed_dims[i]
-                    sample[0, i] = self.rng.normal(self.proposal_mean[i], self.proposal_cov[i, i])
+                else:
-                    if self.lower_bound <= sample[0, i] <= self.upper_bound:
+                    check = False
-                        check = True
+                    while not check:
+                        sample[0, i] = self.rng.normal(self.proposal_mean[i], self.proposal_cov[i, i])
+                        if self.lower_bound[i] <= sample[0, i] <= self.upper_bound[i]:
+                            check = True
 
             samples = np.append(samples, sample, axis=0)
 

src/active_bo_ros/active_bo_ros/BayesianOptimization/BO2D.py

@@ -16,7 +16,8 @@ warnings.filterwarnings('ignore', category=ConvergenceWarning)
 
 
 class BayesianOptimization:
-    def __init__(self, nr_steps, nr_dims, nr_weights, acq='ei', seed=None):
+    def __init__(self, nr_steps, nr_dims, nr_weights, acq='ei', seed=None,
+                 fixed_dims=None, lower_bound=None, upper_bound=None):
         self.acq = acq
         self.episode = 0
 
@@ -25,16 +26,34 @@ class BayesianOptimization:
         self.nr_weights = nr_weights
         self.weights = self.nr_weights * self.nr_dims
 
-        self.lower_bound = -1.0
+        if lower_bound is None:
-        self.upper_bound = 1.0
+            self.lower_bound = [-1.]*nr_dims
+            self.lower_bound_1D = [-1.] * self.weights
+        else:
+            self.lower_bound = lower_bound
+            self.lower_bound_1D = [x for x in lower_bound for _ in range(self.nr_weights)]
+
+        if upper_bound is None:
+            self.upper_bound = [1.]*nr_dims
+            self.upper_bound_1D = [1.] * self.weights
+        else:
+            self.upper_bound = upper_bound
+            self.upper_bound_1D = [x for x in upper_bound for _ in range(self.nr_weights)]
+
         self.seed = seed
 
         self.X = None
         self.Y = None
         self.gp = None
 
+        self.fixed_dims = fixed_dims
+
+        if fixed_dims is not None:
+            self.fixed_dims_1D = self.convert_fixed_dims(fixed_dims, nr_weights)
+
         self.policy_model = GaussianRBF(self.nr_steps, self.nr_weights, self.nr_dims,
-                                           lowerb=self.lower_bound, upperb=self.upper_bound, seed=seed)
+                                        lower_bound=self.lower_bound, upper_bound=self.upper_bound, seed=seed,
+                                        fixed_dims=fixed_dims)
 
         self.acq_sample_size = 100
 
@@ -43,16 +62,26 @@ class BayesianOptimization:
         if acq == "Preference Expected Improvement":
             self.acq_fun = PreferenceExpectedImprovement(self.weights,
                                                          self.acq_sample_size,
-                                                         self.lower_bound,
-                                                         self.upper_bound,
                                                          initial_variance=10.0,
                                                          update_variance=0.05,
-                                                         seed=seed)
+                                                         seed=seed,
+                                                         fixed_dims=self.fixed_dims_1D,
+                                                         lower_bound=self.lower_bound_1D,
+                                                         upper_bound=self.upper_bound_1D,)
 
         self.reset_bo()
 
+    @staticmethod
+    def convert_fixed_dims(fixed_dims, nr_weights):
+        new_fixed_dims = {}
+        for dim, values in fixed_dims.items():
+            for idx, value in enumerate(values):
+                new_dim = dim + idx * nr_weights
+                new_fixed_dims[new_dim] = value
+        return new_fixed_dims
+
     def reset_bo(self):
-        self.gp = GaussianProcessRegressor(Matern(nu=1.5, ), n_restarts_optimizer=5) #length_scale=(1e-8, 1e5)
+        self.gp = GaussianProcessRegressor(Matern(nu=1.5, ), n_restarts_optimizer=5)  # length_scale=(1e-8, 1e5)
         self.best_reward = np.empty((1, 1))
         self.X = np.zeros((1, self.weights), dtype=np.float64)
         self.Y = np.zeros((1, 1), dtype=np.float64)
@@ -117,4 +146,3 @@ class BayesianOptimization:
         x_max = self.X[idx, :].reshape((self.nr_weights, self.nr_dims), order='F')
 
         return y_max, x_max, idx
-

src/active_bo_ros/active_bo_ros/PolicyModel/GaussianModelMultiDim.py

@@ -3,7 +3,7 @@ import numpy as np
 
 
 class GaussianRBF:
-    def __init__(self, nr_steps, nr_weights, nr_dims, lowerb=-1.0, upperb=1.0, seed=None):
+    def __init__(self, nr_steps, nr_weights, nr_dims, lower_bound=None, upper_bound=None, seed=None, fixed_dims=None):
         self.nr_weights = nr_weights
         self.nr_steps = nr_steps
         self.nr_dims = nr_dims
@@ -11,11 +11,20 @@ class GaussianRBF:
         self.weights = None
         self.trajectory = None
 
-        self.lowerb = lowerb
+        if lower_bound is None:
-        self.upperb = upperb
+            self.lower_bound = [-1.]*nr_dims
+        else:
+            self.lower_bound = lower_bound
+
+        if upper_bound is None:
+            self.upper_bound = [1.]*nr_dims
+        else:
+            self.upper_bound = upper_bound
 
         self.rng = np.random.default_rng(seed=seed)
 
+        self.fixed_dims = fixed_dims
+
         # initialize
         self.mid_points = np.linspace(0, self.nr_steps, self.nr_weights)
         if nr_weights > 1:
@@ -30,8 +39,13 @@ class GaussianRBF:
         self.trajectory = np.zeros((self.nr_steps, self.nr_dims))
 
     def random_weights(self):
-        for dim in range(self.nr_dims):
+        for w in range(self.nr_weights):
-            self.weights[:, dim] = self.rng.uniform(self.lowerb, self.upperb, self.nr_weights)
+            # If dim exists in fixed_dims, set weights directly
+            if w in self.fixed_dims:
+                self.weights[w, :] = np.array(self.fixed_dims[w])
+            else:
+                for dim in range(self.nr_dims):
+                    self.weights[w, dim] = self.rng.uniform(self.lower_bound[dim], self.upper_bound[dim], 1)
         return self.weights
 
     def rollout(self):

src/active_bo_ros/active_bo_ros/interactive_bo_robot.py

@@ -101,6 +101,11 @@ class ActiveBOTopic(Node):
                                           self.mainloop_callback,
                                           callback_group=mainloop_callback_group)
 
+        # Franka Experiment
+        self.fixed_dimension = {0: [0.5, -0.3]}
+        self.lower_bound = [0.3, -0.35]
+        self.upper_bound = [0.7, 0.35]
+
     def reset_bo_request(self):
         self.bo_env = None
         self.bo_metric = None
@@ -199,7 +204,10 @@ class ActiveBOTopic(Node):
                 self.BO = BayesianOptimization(self.bo_steps,
                                                self.bo_nr_dims,
                                                self.bo_nr_weights,
-                                               acq=self.bo_acq_fcn)
+                                               acq=self.bo_acq_fcn,
+                                               fixed_dims=self.fixed_dimension,
+                                               lower_bound=self.lower_bound,
+                                               upper_bound=self.upper_bound)
 
                 self.BO.reset_bo()
 
@@ -209,7 +217,7 @@ class ActiveBOTopic(Node):
             if self.init_pending:
 
                 rl_msg = ActiveRLRequest()
-                rl_msg.interactive_run = 2
+                rl_msg.interactive_run = 1
                 rl_msg.weights = self.BO.policy_model.random_weights().flatten('F').tolist()
                 rl_msg.policy = self.BO.policy_model.rollout().flatten('F').tolist()
                 rl_msg.nr_weights = self.bo_nr_weights
@@ -237,6 +245,8 @@ class ActiveBOTopic(Node):
                         env = 're'
                     elif self.bo_env == "Finger":
                         env = 'fin'
+                    elif self.bo_env == "Franka":
+                        env = 'franka'
                     else:
                         raise NotImplementedError
 
@@ -264,9 +274,9 @@ class ActiveBOTopic(Node):
                     np.savetxt(path, data, delimiter=',')
 
                 rl_msg = ActiveRLRequest()
-                rl_msg.interactive_run = 1
+                rl_msg.interactive_run = 2
-                rl_msg.weights = self.BO.policy_model.random_weights().flatten('F').tolist()
+                rl_msg.weights = self.best_policy[best_policy_idx, :, :].flatten('F').tolist()
-                rl_msg.policy = self.BO.policy_model.rollout().flatten('F').tolist()
+                rl_msg.weights = self.best_weights[best_policy_idx, :, :].flatten('F').tolist()
                 rl_msg.nr_weights = self.bo_nr_weights
                 rl_msg.nr_steps = self.bo_steps
                 rl_msg.nr_dims = self.bo_nr_dims
@@ -275,8 +285,6 @@ class ActiveBOTopic(Node):
 
                 self.rl_pending = True
 
-                self.active_rl_pub.publish(rl_msg)
-
                 self.get_logger().info('Responding: Active BO')
                 self.active_bo_pub.publish(bo_response)
                 self.reset_bo_request()
@@ -319,16 +327,12 @@ class ActiveBOTopic(Node):
 
                             rl_msg = ActiveRLRequest()
                             rl_msg.interactive_run = 0
-                            rl_msg.weights = self.BO.policy_model.random_weights().flatten('F').tolist()
                             rl_msg.policy = self.BO.policy_model.rollout().flatten('F').tolist()
+                            rl_msg.weights = old_weights.flatten('F').tolist()
                             rl_msg.nr_weights = self.bo_nr_weights
                             rl_msg.nr_steps = self.bo_steps
                             rl_msg.nr_dims = self.bo_nr_dims
 
-                            self.active_rl_pub.publish(rl_msg)
-
-                            self.rl_pending = True
-
                             self.active_rl_pub.publish(rl_msg)
                             self.rl_pending = True
 
@@ -338,8 +342,8 @@ class ActiveBOTopic(Node):
 
                             rl_msg = ActiveRLRequest()
                             rl_msg.interactive_run = 1
-                            rl_msg.weights = self.BO.policy_model.random_weights().flatten('F').tolist()
                             rl_msg.policy = self.BO.policy_model.rollout().flatten('F').tolist()
+                            rl_msg.weights = x_next.flatten('F').tolist()
                             rl_msg.nr_weights = self.bo_nr_weights
                             rl_msg.nr_steps = self.bo_steps
                             rl_msg.nr_dims = self.bo_nr_dims