diff --git a/kine_ctrl/rm75_kine_qp.py b/kine_ctrl/rm75_kine_qp.py
index ce5e9d0..b1b7796 100644
--- a/kine_ctrl/rm75_kine_qp.py
+++ b/kine_ctrl/rm75_kine_qp.py
@@ -218,10 +218,7 @@ class KinematicsSolver():
         else:
             # Try different initial configurations
             initial_guesses.append([0.1] * 7)  # Zero config
-            initial_guesses.append([radians(30), radians(45), radians(30),
-                                    radians(-45), radians(30), radians(-30), 0])
-            initial_guesses.append([radians(-30), radians(45), radians(-30),
-                                    radians(45), radians(30), radians(30), 0])
+
 
         best_solution = None
         best_error = float('inf')
@@ -247,7 +244,7 @@ class KinematicsSolver():
                 self.data,
                 q,
                 ee_frame_id,
-                pin.ReferenceFrame.LOCAL_WORLD_ALIGNED
+                pin.ReferenceFrame.LOCAL
             )
 
             pin.forwardKinematics(self.model, self.data, q)
@@ -268,7 +265,6 @@ class KinematicsSolver():
                 pin.framesForwardKinematics(self.model, self.data, q)
 
                 # Get current end-effector placement
-
                 current_placement = self.data.oMf[ee_frame_id]
 
                 # Compute error
@@ -293,7 +289,7 @@ class KinematicsSolver():
                     self.model,
                     self.data,
                     ee_frame_id,
-                    pin.ReferenceFrame.LOCAL_WORLD_ALIGNED
+                    pin.ReferenceFrame.LOCAL
                 )
 
                 # =========================
@@ -301,7 +297,7 @@ class KinematicsSolver():
                 # =========================
                 w_posture = 0.0
 
-                J_eff = J # -pin.Jlog6(error_SE3) @ J
+                J_eff = pin.Jlog6(error_SE3) @ J #J #
 
                 H = J_eff.T @ self.W @ J_eff
 
@@ -376,7 +372,7 @@ class KinematicsSolver():
                     break
 
                 # Apply joint limits with scaling
-                alpha = 0.2
+                alpha = 1.0
                 q = pin.integrate(self.model, q, alpha * dq)
 
                 prev_error = error_norm