Reinforcement Learning-Based Transition Control of a Mini Rotary Double Inverted Pendulum: A MuJoCo-Based Approach

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This paper proposes a MuJoCo-based sim-to-real reinforcement learning approach for transition control of a mini rotary double inverted pendulum (MRDIP). Transition control between multiple equilibrium points of the MRDIP is challenging because of its underactuated, nonlinear, and strongly coupled dynamics. To address this problem, a direct-drive MRDIP system is developed to enable torque-controlled actuation, and a MuJoCo model is constructed using CAD-based physical parameters and experimentally identified dynamic parameters. Based on the identified model, transition control policies for multiple equilibrium points are trained using the Truncated Quantile Critics (TQC) algorithm. The trained policies are directly implemented on the physical MRDIP and evaluated through successive equilibrium-point transitions without resetting the system between transitions. Experimental results show that the policies trained in MuJoCo successfully perform the intended transitions on the physical system. These results demonstrate the feasibility of the proposed MuJoCo-based framework for sim-to-real transition control of the MRDIP.