Merge branch 'modified-circle' into whole-body-fixed-base

config/walk_parameters.yaml

@@ -25,19 +25,19 @@ 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: "step" # name of the movement to perform
+    movement: "circle" # 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+
     # Kd_main: [0., 0., 0.]  # Derivative gains for the PD+
 #     Kff_main: 0.0  # Feedforward torques multiplier for the PD+
     Kp_main: [3, 3, 3]  # Proportional gains for the PD+
-    Kd_main: [0.5, 0.5, 0.5]  # Derivative gains for the PD+
+    Kd_main: [0.3, 0.3, 0.3]  # Derivative gains for the PD+
     Kff_main: 1.0  # Feedforward torques multiplier for the PD+
 
     # Parameters of Gait
     N_periods: 1
-    gait: [80, 0, 0, 0, 0]  # Initial gait matrix
+    gait: [100, 0, 0, 0, 0]  # Initial gait matrix
 
     # Parameters of Joystick
     gp_alpha_vel: 0.003  # Coefficient of the low pass filter applied to gamepad velocity

python/quadruped_reactive_walking/WB_MPC/ProblemData.py

@@ -170,13 +170,13 @@ class ProblemDataFull(problemDataAbstract):
 
         # Cost function weights
         self.mu = 0.7
-        self.foot_tracking_w = 1e5
+        self.foot_tracking_w = 1e4
         self.friction_cone_w = 1e3
         self.control_bound_w = 1e3
-        self.control_reg_w = 1e2
+        self.control_reg_w = 1e0
         self.state_reg_w = np.array(
             [1e2] * 3 + [1e-2] * 3 + [1e2] * 6 +
-            [1e1] * 3 + [1e-0] * 3 + [1e1] * 6
+            [1e1] * 3 + [1e0] * 3 + [1e1] * 6
         )
         self.terminal_velocity_w = np.array([0] * 12 + [1e3] * 12)