diff --git a/config/walk_parameters.yaml b/config/walk_parameters.yaml
index db4ac965ab687a06ea23df83ca825aa17657dd99..393ca06024e7dc75423d6c02923114f22fd69e73 100644
--- a/config/walk_parameters.yaml
+++ b/config/walk_parameters.yaml
@@ -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: true # 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+
diff --git a/python/quadruped_reactive_walking/WB_MPC/ProblemData.py b/python/quadruped_reactive_walking/WB_MPC/ProblemData.py
index 7127207212afd325c11024b38c93f9da1a638ef3..dfcd025a31938f2ab84a0b532456f8e82d52ac7a 100644
--- a/python/quadruped_reactive_walking/WB_MPC/ProblemData.py
+++ b/python/quadruped_reactive_walking/WB_MPC/ProblemData.py
@@ -170,7 +170,7 @@ class ProblemDataFull(problemDataAbstract):
 
         # Cost function weights
         self.mu = 0.7
-        self.foot_tracking_w = 1e4
+        self.foot_tracking_w = 1e5
         self.friction_cone_w = 1e3
         self.control_bound_w = 1e3
         self.control_reg_w = 1e2