Ready for merge

config/walk_parameters.yaml

@@ -6,8 +6,8 @@ robot:
     LOGGING: true  # Enable/disable logging during the experiment
     PLOTTING: true  # Enable/disable automatic plotting at the end of the experiment
     DEMONSTRATION: false  # Enable/disable demonstration functionalities
-    SIMULATION: false  # Enable/disable PyBullet simulation or running on real robot
-    enable_pyb_GUI: false  # Enable/disable PyBullet GUI
+    SIMULATION: true  # Enable/disable PyBullet simulation or running on real robot
+    enable_pyb_GUI: true  # Enable/disable PyBullet GUI
     envID: 0  # Identifier of the environment to choose in which one the simulation will happen
     use_flat_plane: true  # If True the ground is flat, otherwise it has bumps
     predefined_vel: true  # If we are using a predefined reference velocity (True) or a joystick (False)
@@ -25,7 +25,7 @@ robot:
     dt_mpc: 0.0014  # 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
 #     Kp_main: [0.0, 0.0, 0.0]  # Proportional gains for the PD+
-    Kp_main: [4, 4, 4]  # Proportional gains for the PD+
+    Kp_main: [3, 3, 3]  # Proportional gains for the PD+
 #     Kd_main: [0., 0., 0.]  # Derivative gains for the PD+
     Kd_main: [0.3, 0.3, 0.3]  # Derivative gains for the PD+
 #     Kff_main: 0.0  # Feedforward torques multiplier for the PD+

python/quadruped_reactive_walking/WB_MPC/ProblemData.py

@@ -116,7 +116,7 @@ class ProblemDataFull(problemDataAbstract):
         #self.friction_cone_w = 1e3 * 0
         self.control_bound_w = 1e3
         self.control_reg_w = 1e0
-        self.state_reg_w = np.array([1e-5] * 3 + [ 3* 1e-1]*3)
+        self.state_reg_w = np.array([1e-3] * 3 + [ 3* 1e-1]*3)
         self.terminal_velocity_w = np.array([0] * 3 + [1e3] * 3 )
 
         self.q0_reduced = self.q0[10:13]