VIMPPI: Enhancing Model Predictive Path Integral Control with Variational Integration for Underactuated Systems

-- This paper presents VIMPPI, a novel control approach for underactuated double pendulum systems developed for the AI Olympics competition. We enhance the Model Predictive Path Integral framework by incorporating variational integration techniques, enabling longer planning horizons without additional computational cost. Operating at 500-700 Hz with control interpolation and disturbance detection mechanisms, VIMPPI substantially outperforms both baseline methods and alternative MPPI implementations. The AI Olympics with RealAIGym [1] competition challenges participants to develop controllers for complex robotic systems. This year's task focused on designing controllers for underactuated double pendulum systems -- the pendubot and acrobot -- with emphasis on maintaining the upper equilibrium position from various initial states [2].