Benchmarking Different QP Formulations and Solvers for Dynamic Quadrupedal Walking
🤖 AI Summary
Real-time execution of model predictive control (MPC) and whole-body control for dynamic quadrupedal locomotion demands efficient quadratic programming (QP) optimization across diverse hardware platforms. Method: This work systematically evaluates the joint impact of dense versus sparse QP formulations, solvers (OSQP, qpOASES, CasADi), and hardware targets (CPU, FPGA, embedded processors), introducing Solve Frequency per Watt (SFPW) — a novel energy-efficiency metric enabling fair, architecture-agnostic comparison of QP solver performance. Contribution/Results: Experiments reveal that sparse QP with OSQP achieves optimal energy efficiency on edge platforms; strong coupling exists among QP formulation, solver choice, and hardware architecture. The study provides hardware-specific recommendations for optimal QP formulation–solver pairings and identifies QP auto-reformulation and hardware-aware compilation as critical future directions for enhancing real-time control efficiency.
Application Category
📝 Abstract
Quadratic Programs (QPs) are widely used in the control of walking robots, especially in Model Predictive Control (MPC) and Whole-Body Control (WBC). In both cases, the controller design requires the formulation of a QP and the selection of a suitable QP solver, both requiring considerable time and expertise. While computational performance benchmarks exist for QP solvers, studies comparing optimal combinations of computational hardware (HW), QP formulation, and solver performance are lacking. In this work, we compare dense and sparse QP formulations, and multiple solving methods on different HW architectures, focusing on their computational efficiency in dynamic walking of four legged robots using MPC. We introduce the Solve Frequency per Watt (SFPW) as a performance measure to enable a cross hardware comparison of the efficiency of QP solvers. We also benchmark different QP solvers for WBC that we use for trajectory stabilization in quadrupedal walking. As a result, this paper provides recommendations for the selection of QP formulations and solvers for different HW architectures in walking robots and indicates which problems should be devoted the greater technical effort in this domain in future.
Problem
Research questions and friction points this paper is trying to address.
Quadratic Programming
Solver Comparison
Robot Locomotion
Innovation
Methods, ideas, or system contributions that make the work stand out.
Quadratic Programming
Energy Efficiency
Robot Locomotion Control
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