Safe Approximate Nonlinear Model Predictive Control Without Expert Demonstrations
Overview
This research introduces Sequential Approximate MPC (Seq-AMPC), a label-free recurrent framework for nonlinear model predictive control that generates complete control sequences using shared parameters across the prediction horizon.
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Reported quantity · mean closed-loop cost ratio difference from NMPC: 0.3 other · Basis not reported
“itation training. Across four benchmarks, training takes 0.2-1.7 GPU hours per policy without expert control labels. The filtered controller applies 97.2-99.1% of neural proposals and achieves mean closed-loop cost ratios within 0.3% of NMPC on jointly completed episodes. Tests with suboptimal feasible initial plans further demonstrate the benefit of neural replanning.”
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- arXiv Robotics — research abstractsSignalSafe Approximate Nonlinear Model Predictive Control Without Expert Demonstrations
This research introduces Sequential Approximate MPC (Seq-AMPC), a label-free recurrent framework for nonlinear model predictive control that generates complete control sequences using shared parameters across the prediction horizon.
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