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About this paper
Franek Stark, Jakob Middelberg, Dennis Mronga, Shubham Vyas, Frank Kirchner
Researchers from the German Research Center for Artificial Intelligence (DFKI) performed a comprehensive benchmark of Quadratic Program (QP) formulations and solvers for dynamic quadrupedal walking. The study, which introduced "Solve Frequency per Watt (SFPW)" as a novel efficiency metric, demonstrated that ARM architectures offer superior energy efficiency for robotic control, while sparse formulations with IPM-based solvers like HPIPM provide the fastest and most robust performance for Model Predictive Control (MPC).
Key insights
- IPM-based sparse solvers, particularly HPIPM, are generally the fastest and most robust for MPC, especially for longer prediction horizons, exhibiting stable performance across various dynamic scenarios.
- ARM-based single-board computers (e.g., Jetson Orin) offer significantly higher energy efficiency (SFPW) compared to x86 systems, making them highly suitable for power-constrained autonomous robot applications.
- For Whole-Body Control, most open-source solvers perform adequately with generally low computation times, suggesting that engineering efforts should prioritize robust problem formulations over specific solver selection in this context.
Summary by alphaXiv.
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