  This paper introduces a novel zero-shot motion planning method that allows
users to quickly design smooth robot motions in Cartesian space. A B\'ezier
curve-based Cartesian plan is transformed into a joint space trajectory by our
neuro-inspired inverse kinematics (IK) method CycleIK, for which we enable
platform independence by scaling it to arbitrary robot designs. The motion
planner is evaluated on the physical hardware of the two humanoid robots NICO
and NICOL in a human-in-the-loop grasping scenario. Our method is deployed with
an embodied agent that is a large language model (LLM) at its core. We
generalize the embodied agent, that was introduced for NICOL, to also be
embodied by NICO. The agent can execute a discrete set of physical actions and
allows the user to verbally instruct various different robots. We contribute a
grasping primitive to its action space that allows for precise manipulation of
household objects. The new CycleIK method is compared to popular numerical IK
solvers and state-of-the-art neural IK methods in simulation and is shown to be
competitive with or outperform all evaluated methods when the algorithm runtime
is very short. The grasping primitive is evaluated on both NICOL and NICO
robots with a reported grasp success of 72% to 82% for each robot,
respectively.
