TurboMPC: Fast, Scalable, and Differentiable Model Predictive Control on the GPU
Robotics increasingly relies on GPUs for parallel simulation, large-scale learning, and neural-network inference. For model predictive control (MPC) to scale with this paradigm, solvers must run efficiently on this hardware while remaining fast, differentiable, and compatible with expressive MPC formulations used in robotics. We present TurboMPC, a differentiable MPC solver that runs entirely on the GPU and supports state and control inequality constraints, implicit integrators, cross-time-coupled costs, and slack variables. TurboMPC combines sequential quadratic programming (SQP), an alternating direction method of multipliers (ADMM) inner solver, implicit differentiation, and a co-desig...
| Authors | Gabriel Bravo-Palacios, Jianghan Zhang, Zachary Pestrikov et al. |
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| Primary category | cs.RO |
| Published | 23 June 2026 |
| Last updated at source | 23 June 2026 |
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