A Deep Reinforcement Learning Approach for Dynamically Stable Inverse Kinematics of Humanoid Robots
This addresses the challenge of real-time balance maintenance in humanoid robots, which is crucial for their practical deployment, though it is an incremental improvement by applying existing DRL methods to a specific domain.
The paper tackled the problem of generating dynamically stable inverse kinematics trajectories for humanoid robots using deep reinforcement learning, achieving 90% accuracy in solving inverse kinematics while maintaining balance in double support phase.
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.