# Inverse Kinematics for Neuro-Robotic Grasping with Humanoid Embodied Agents

arXiv: https://arxiv.org/abs/2404.08825 · HTML: https://mlsanity.com/paper/2404.08825/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 41 references that are in the map.
- First posted on arXiv 12 Apr 2024.

## Builds on (most cited first, 18 of 18)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Open-vocabulary Object Detection via Vision and Language Knowledge Distillation](https://mlsanity.com/paper/2104.13921/) (2021)
- [HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2011.14672/) (2020)
- [Analyzing Inverse Problems with Invertible Neural Networks](https://mlsanity.com/paper/1808.04730/) (2018)
- [Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study](https://mlsanity.com/paper/1404.3785/) (2014)
- [Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners](https://mlsanity.com/paper/1907.06013/) (2019)
- [NIKI: Neural Inverse Kinematics with Invertible Neural Networks for 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2305.08590/) (2023)
- [ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics](https://mlsanity.com/paper/2106.01981/) (2021)
- [IKFlow: Generating Diverse Inverse Kinematics Solutions](https://mlsanity.com/paper/2111.08933/) (2021)
- [NICOL: A Neuro-inspired Collaborative Semi-humanoid Robot that Bridges Social Interaction and Reliable Manipulation](https://mlsanity.com/paper/2305.08528/) (2023)
- [SMPL-IK: Learned Morphology-Aware Inverse Kinematics for AI Driven Artistic Workflows](https://mlsanity.com/paper/2208.08274/) (2022)
- [CycleIK: Neuro-inspired Inverse Kinematics](https://mlsanity.com/paper/2307.11554/) (2023)
- [CppFlow: Generative Inverse Kinematics for Efficient and Robust Cartesian Path Planning](https://mlsanity.com/paper/2309.09102/) (2023)
- [NODE IK: Solving Inverse Kinematics with Neural Ordinary Differential Equations for Path Planning](https://mlsanity.com/paper/2209.00498/) (2022)
- [Euclidean Equivariant Models for Generative Graphical Inverse Kinematics](https://mlsanity.com/paper/2307.01902/) (2023)

## Built on by (most cited first, 7 of 7)

- [ABPolicy: Asynchronous B-Spline Flow Policy for Real-Time and Smooth Robotic Manipulation](https://mlsanity.com/paper/2602.23901/) (2026)
- [Robots Can Multitask Too: Integrating a Memory Architecture and LLMs for Enhanced Cross-Task Robot Action Generation](https://mlsanity.com/paper/2407.13505/) (2024)
- [DIRIGENt: End-To-End Robotic Imitation of Human Demonstrations Based on a Diffusion Model](https://mlsanity.com/paper/2501.16800/) (2025)
- [Shaken, Not Stirred: A Novel Dataset for Visual Understanding of Glasses in Human-Robot Bartending Tasks](https://mlsanity.com/paper/2503.04308/) (2025)
- [Towards Bio-Inspired Robotic Trajectory Planning via Self-Supervised RNN](https://mlsanity.com/paper/2507.02171/) (2025)
- [Generating and Customizing Robotic Arm Trajectories using Neural Networks](https://mlsanity.com/paper/2506.20259/) (2025)
- [MorphIK: Morphology-Conditioned Neural Inverse Kinematics for Unknown Robots](https://mlsanity.com/paper/2609.29908/) (2026)

## Abstract (arXiv)

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 embody 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 updated 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.