diff --git a/src/adam/pytorch/computation_batch.py b/src/adam/pytorch/computation_batch.py
index aef57056..b5e96a10 100644
--- a/src/adam/pytorch/computation_batch.py
+++ b/src/adam/pytorch/computation_batch.py
@@ -16,7 +16,7 @@
 
 class KinDynComputationsBatch:
     """This is a small class that retrieves robot quantities using Jax for Floating Base systems.
-    These functions are vmapped and jit compiled and passed to jax2torch to convert them to torch functions.
+    These functions are vmapped and jit compiled and passed to jax2torch to convert them to PyTorch functions.
     """
 
     def __init__(
@@ -233,7 +233,7 @@ def fun(base_transform, joint_positions):
                 frame, base_transform, joint_positions
             ).array
 
-        vmapped_fun = jax.vmap(fun)
+        vmapped_fun = jax.vmap(fun, in_axes=(0, 0))
         jit_vmapped_fun = jax.jit(vmapped_fun)
         self.funcs[f"forward_kinematics_{frame}"] = jax2torch(jit_vmapped_fun)
         return self.funcs[f"forward_kinematics_{frame}"]
@@ -269,7 +269,7 @@ def jacobian_fun(self, frame: str):
         def fun(base_transform, joint_positions):
             return self.rbdalgos.jacobian(frame, base_transform, joint_positions).array
 
-        vmapped_fun = jax.vmap(fun)
+        vmapped_fun = jax.vmap(fun, in_axes=(0, 0))
         jit_vmapped_fun = jax.jit(vmapped_fun)
         self.funcs[f"jacobian_{frame}"] = jax2torch(jit_vmapped_fun)
         return self.funcs[f"jacobian_{frame}"]
@@ -398,7 +398,7 @@ def fun(base_transform, joint_positions):
                 self.g,
             ).array.squeeze()
 
-        vmapped_fun = jax.vmap(fun)
+        vmapped_fun = jax.vmap(fun, in_axes=(0, 0))
         jit_vmapped_fun = jax.jit(vmapped_fun)
         self.funcs["gravity_term"] = jax2torch(jit_vmapped_fun)
         return self.funcs["gravity_term"]
@@ -430,7 +430,7 @@ def CoM_position_fun(self):
         def fun(base_transform, joint_positions):
             return self.rbdalgos.CoM_position(base_transform, joint_positions).array
 
-        vmapped_fun = jax.vmap(fun)
+        vmapped_fun = jax.vmap(fun, in_axes=(0, 0))
         jit_vmapped_fun = jax.jit(vmapped_fun)
         self.funcs["CoM_position"] = jax2torch(jit_vmapped_fun)
         return self.funcs["CoM_position"]