ZipDrift: Generating Multimodal Short-Horizon MPC Costs with One-Step Drifting Models
Overview
This research presents ZipDrift, a technique that uses one-step drifting models to generate multimodal corrections for short-horizon model predictive control (MPC) costs, mimicking long-horizon expert behavior.
Generated from attributed reports · Updated 2 days ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · contexts evaluated: 1000 other · Basis not reported
“learned critic, and the model-based MPC then enforces dynamics and constraints. In a kinematic overtaking benchmark derived from ZipMPC, more than half of the contexts are bimodal and the manual short-horizon MPC crashes in 28% of them. With an eight-dimensional stagewise correction, evaluated on 1000 fresh contexts and five training seeds, ZipDrift with eight candidates crashes in 0.74% of episodes, compared with 3.”
Exact source · revision 3Source owner not reported
Reported quantity · training seeds: 5 other · Basis not reported
“learned critic, and the model-based MPC then enforces dynamics and constraints. In a kinematic overtaking benchmark derived from ZipMPC, more than half of the contexts are bimodal and the manual short-horizon MPC crashes in 28% of them. With an eight-dimensional stagewise correction, evaluated on 1000 fresh contexts and five training seeds, ZipDrift with eight candidates crashes in 0.74% of episodes, compared with 3.”
Exact source · revision 3Source owner not reported
Reported quantity · candidates generated: 8 other · Basis not reported
“learned critic, and the model-based MPC then enforces dynamics and constraints. In a kinematic overtaking benchmark derived from ZipMPC, more than half of the contexts are bimodal and the manual short-horizon MPC crashes in 28% of them. With an eight-dimensional stagewise correction, evaluated on 1000 fresh contexts and five training seeds, ZipDrift with eight candidates crashes in 0.74% of episodes, compared with 3.”
Exact source · revision 3Source owner not reported
Evidence dependency changes (1)
- 2026-11-08T06:14:14.277Z · evidence updated · source revision 3. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalZipDrift: Generating Multimodal Short-Horizon MPC Costs with One-Step Drifting Models
This research presents ZipDrift, a technique that uses one-step drifting models to generate multimodal corrections for short-horizon model predictive control (MPC) costs, mimicking long-horizon expert behavior.
Event coverage history
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