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

TL;DR

This paper introduces a deep reinforcement learning method that computes real-time, dynamically stable inverse kinematics solutions for humanoid robots, ensuring balance and accuracy in complex configurations.

Contribution

It presents a novel approach using Deep Deterministic Policy Gradient to generate stable joint trajectories across the entire configuration space of a humanoid robot.

Findings

Achieved 90% accuracy in inverse kinematics solutions.

Maintained balance during double support phase.

Validated on a 27-DoF humanoid upper body.

Abstract

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.