Reward Planning For Underactuated Robotic Systems With Parameters Uncertainty: Greedy-Divide and Conquer
Sinan Ibrahim, S. M. Ahsan Kazmi, Dmitrii Dobriborsci, Roman Zashchitin, Mostafa Mostafa, Pavel Osinenko · 2024
Traditional control approaches for robotic systems, such as linear quadratic regulator (LQR) or model predictive control (MPC), often rely on a known model of the environment. However, in the real world, uncertainty is a common feature of control problems hence models have imperfections. In this work, we address reward engineering for underactuated robotic systems with parameter uncertainty. We introduce a novel reinforcement learning (RL) method to plan the reward function, specifically designed for underactuated robotic systems with parameter uncertainty. We present and validate a new algorithm called Greedy-Divide and Conquer. We implement this algorithm with a single RL agent to address the challenge of swinging up and balancing a Pendubot system with uncertain parameters and give another example with a 2D-Drone with body mass uncertainty. Our ultimate objective is to enhance the system’s ability to adapt and perform reliably in the face of varying uncertainties.