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.

翻译：本文提出一种新颖的零样本运动规划方法，使用户能够快速设计笛卡尔空间中的平滑机器人运动。通过神经启发式逆运动学（IK）方法CycleIK，我们将基于贝塞尔曲线的笛卡尔规划转化为关节空间轨迹，并通过缩放使其适用于任意机器人设计，实现了平台无关性。该运动规划器在两个人形机器人NICO和NICOL的物理硬件上，于人在环抓取场景中进行了评估。我们的方法部署在以大型语言模型（LLM）为核心的具身智能体上。我们将最初为NICOL引入的具身智能体推广至NICO平台，该智能体可执行一组离散的物理动作，并允许用户通过口头指令操控不同种类的机器人。我们在其动作空间中增加了一种抓取基元，用于对家庭物品进行精确操控。通过仿真实验，我们将新的CycleIK方法与流行的数值IK求解器及最先进的神经IK方法进行比较，结果表明当算法运行时间极短时，该方法具有竞争力或优于所有评估方法。该抓取基元在NICOL和NICO机器人上的抓取成功率分别为72%和82%。