Circular motion

12DOF
CL
Interpolation
NoPush

config/walk_parameters.yaml

@@ -6,12 +6,12 @@ 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: true  # Enable/disable PyBullet simulation or running on real robot
+    SIMULATION: false  # 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)
-    N_SIMULATION: 10000  # Number of simulated wbc time steps
+    N_SIMULATION: 15000  # Number of simulated wbc time steps
     enable_corba_viewer: false  # Enable/disable Corba Viewer
     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
@@ -24,7 +24,7 @@ robot:
     dt_wbc: 0.001  # Time step of the whole body control
     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: true # true to interpolate the impedance quantities between nodes of the MPC
+    save_guess: false # true to interpolate the impedance quantities between nodes of the MPC
     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

python/quadruped_reactive_walking/WB_MPC/ProblemData.py

@@ -173,9 +173,9 @@ class ProblemDataFull(problemDataAbstract):
         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(
-            [1e1] * 3 + [1e-2] * 3 + [1e1] * 6 +
+            [1e2] * 3 + [1e-2] * 3 + [1e2] * 6 +
             [1e1] * 3 + [1e0] * 3 + [1e1] * 6
         )
         self.terminal_velocity_w = np.array([0] * 12 + [1e3] * 12)