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

Authors
Igor Alentev, Lev Kozlov, Ivan Domrachev, Simeon Nedelchev, Jee-Hwan Ryu
Year
2025
Preprint
Algorithm
VIMPPI
Source
arXiv
Identifiers
arXiv 2505.05507

Abstract

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.

Summary

Enhances MPPI with variational integration (VI) to accurately simulate the system evolution inside an MPPI over long planning horizons.

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Tags

Model predictive path integral control Trajectory optimization Sampling-based control Variational integration Underactuated systems

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