Implement Forward Kinematics with jax.lax.scan

src/jaxsim/physics/algos/forward_kinematics.py

@@ -1,3 +1,6 @@
+from typing import Tuple
+
+import jax
 import jax.numpy as jnp
 import numpy as np
 
@@ -21,21 +24,43 @@ def forward_kinematics_model(
     r = jnp.vstack(x_fb[4:7])
     W_X_0 = jnp.linalg.inv(Plucker.from_rot_and_trans(Quaternion.to_dcm(qn), r))
 
+    # This is the 6D velocity transform from i-th link frame to the world frame
     W_X_i = jnp.zeros(shape=[model.NB, 6, 6])
     W_X_i = W_X_i.at[0].set(W_X_0)
 
     i_X_pre = model.joint_transforms(q=q)
     pre_X_λi = model.tree_transforms
+
+    # This is the parent-to-child 6D velocity transforms of all links
     i_X_λi = jnp.zeros_like(i_X_pre)
 
-    for i in np.arange(start=1, stop=model.NB):
+    # Parent array mapping: i -> λ(i).
+    # Exception: λ(0) must not be used, it's initialized to -1.
+    λ = model.parent
+
+    PropagateKinematicsCarry = Tuple[jtp.MatrixJax, jtp.MatrixJax]
+    propagate_kinematics_carry = (i_X_λi, W_X_i)
+
+    def propagate_kinematics(
+        carry: PropagateKinematicsCarry, i: jtp.Int
+    ) -> Tuple[PropagateKinematicsCarry, None]:
+
+        i_X_λi, W_X_i = carry
 
         i_X_λi_i = i_X_pre[i] @ pre_X_λi[i]
         i_X_λi = i_X_λi.at[i].set(i_X_λi_i)
 
-        W_X_i_i = W_X_i[model.parent[i]] @ jnp.linalg.inv(i_X_λi[i])
+        W_X_i_i = W_X_i[λ[i]] @ jnp.linalg.inv(i_X_λi[i])
         W_X_i = W_X_i.at[i].set(W_X_i_i)
 
+        return (i_X_λi, W_X_i), None
+
+    (_, W_X_i), _ = jax.lax.scan(
+        f=propagate_kinematics,
+        init=propagate_kinematics_carry,
+        xs=np.arange(start=1, stop=model.NB),
+    )
+
     return jnp.stack([Plucker.to_transform(X) for X in list(W_X_i)])