An SQP-based MPC controller with inverse-dynamics formulation swings up and stabilizes underactuated double pendulums on hardware, with 100% success in three of four scenarios but 70% on the disturbed Acrobot.
Velocity-History-Based Soft Actor-Critic Tackling IROS'24 Competition "AI Olympics with RealAIGym"
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abstract
The ``AI Olympics with RealAIGym'' competition challenges participants to stabilize chaotic underactuated dynamical systems with advanced control algorithms. In this paper, we present a novel solution submitted to IROS'24 competition, which builds upon Soft Actor-Critic (SAC), a popular model-free entropy-regularized Reinforcement Learning (RL) algorithm. We add a `context' vector to the state, which encodes the immediate history via a Convolutional Neural Network (CNN) to counteract the unmodeled effects on the real system. Our method achieves high performance scores and competitive robustness scores on both tracks of the competition: Pendubot and Acrobot.
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Real-Time Nonlinear MPC via Sequential Quadratic Programming with Structure-Exploiting ADMM and Interior-Point Methods for Underactuated Double-Pendulum Swing-Up
An SQP-based MPC controller with inverse-dynamics formulation swings up and stabilizes underactuated double pendulums on hardware, with 100% success in three of four scenarios but 70% on the disturbed Acrobot.