# A Deep Reinforcement Learning Approach for Dynamically Stable Inverse Kinematics of Humanoid Robots

arXiv: https://arxiv.org/abs/1801.10425 · HTML: https://mlsanity.com/paper/1801.10425/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2018.

## Built on by (most cited first, 3 of 3)

- [Physics-Driven ML-Based Modelling for Correcting Inverse Estimation](https://mlsanity.com/paper/2309.13985/) (2023)
- [The Importance of Models in Data Analysis with Small Human Movement Datasets -- Inspirations from Neurorobotics Applied to Posture Control of Humanoids and Humans](https://mlsanity.com/paper/2102.02543/) (2021)
- [ImaGGen: Zero-Shot Generation of Co-Speech Semantic Gestures Grounded in Language and Image Input](https://mlsanity.com/paper/2510.17617/) (2025)

## Abstract (arXiv)

Real time calculation of inverse kinematics (IK) with dynamically stable configuration is of high necessity in humanoid robots as they are highly susceptible to lose balance. This paper proposes a methodology to generate joint-space trajectories of stable configurations for solving inverse kinematics using Deep Reinforcement Learning (RL). Our approach is based on the idea of exploring the entire configuration space of the robot and learning the best possible solutions using Deep Deterministic Policy Gradient (DDPG). The proposed strategy was evaluated on the highly articulated upper body of a humanoid model with 27 degree of freedom (DoF). The trained model was able to solve inverse kinematics for the end effectors with 90% accuracy while maintaining the balance in double support phase.