Robot Control Using Model-Based Reinforcement Learning With Inverse Kinematics

This work investigates the complications of robotic learning using reinforcement learning (RL). While RL has enormous potential for solving complex tasks its major caveat is the computation cost- and time-intensive training procedure. This work aims to address this issue by introducing a humanlike thinking and acting paradigm to a RL approach. It utilizes model-based deep RL for planning (think) coupled with inverse kinematics (IK) for the execution of actions (act). The approach was developed and tested using a Franka Emika Panda robot model in a simulated environment using the PyBullet physics engine Bullet. It was tested on three different simulated tasks and then compared to the conventional method using RL-only to learn the same tasks. The results show that the RL algorithm with IK converges significantly faster and with higher quality than the applied conventional approach, achieving 98%, 99% and 98% success rates for tasks 1-3 respectively. This work verifies its benefit for use of RL-IK with multi-joint robots.