still have to work on multiprocessing

config/walk_parameters.yaml

@@ -13,7 +13,7 @@ robot:
     predefined_vel: true  # If we are using a predefined reference velocity (True) or a joystick (False)
     N_SIMULATION: 10000  # Number of simulated wbc time steps
     enable_corba_viewer: false  # Enable/disable Corba Viewer
-    enable_multiprocessing: false  # Enable/disable running the MPC in another process in parallel of the main loop
+    enable_multiprocessing: true  # Enable/disable running the MPC in another process in parallel of the main loop
     perfect_estimator: true  # Enable/disable perfect estimator by using data directly from PyBullet
 
     # General control parameters
@@ -25,7 +25,7 @@ robot:
     dt_mpc: 0.015  # Time step of the model predictive control
     type_MPC: 3  # Which MPC solver you want to use: 0 for OSQP MPC, 1, 2, 3 for Crocoddyl MPCs
     save_guess: false # true to interpolate the impedance quantities between nodes of the MPC
-    movement: "circle" # name of the movement to perform
+    movement: "step" # name of the movement to perform
     interpolate_mpc: true # true to interpolate the impedance quantities between nodes of the MPC
     interpolation_type: 3 # 0,1,2,3 decide which kind of interpolation is used
     # Kp_main: [0.0, 0.0, 0.0]  # Proportional gains for the PD+

python/quadruped_reactive_walking/WB_MPC/ProblemData.py

@@ -175,12 +175,12 @@ class ProblemDataFull(problemDataAbstract):
         self.foot_tracking_w = 1e4
         # self.friction_cone_w = 1e3 * 0
         self.control_bound_w = 1e3
-        self.control_reg_w = 1e0
+        self.control_reg_w = 1e-1
         self.state_reg_w = np.array([1e1] * 3
-                                    + [1e-1] * 3
+                                    + [1e-2] * 3
                                     + [1e1] * 6
                                     + [1e1] * 3
-                                    + [1e0] * 3
+                                    + [3*1e-1] * 3
                                     + [1e1] * 6
                                     )
         self.terminal_velocity_w = np.array([0] * 12 + [1e3] * 12)