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Primitive-Informed Sampling-Based MPC for Multi-Fingered Dexterous Manipulation

US & West · 14 Sept

We present a primitive-informed sampling-based model predictive control (MPC) framework for multi-fingered dexterous manipulation. Sampling-based MPC evaluates candidate control trajectories through forward simulation without requiring gradients through complex contact dynamics.

Original headline: Primitive-Informed Sampling-Based MPC for Multi-Fingered Dexterous Manipulation

Read the original: arXiv API — полнотекстовый поиск "digital twin"

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Primitive-Informed Sampling-Based MPC for Multi-Fingered Dexterous Manipulation

Related

Model-Free Control for Residential Heating: Deployment and Simulation of Nonlinear Data-Enabled Predictive Control

ForceTwin: Physics-informed Digital Twins for Robotic Manipulation from Instrumented Human Interaction

Опубликованы итоги форсайт-сессии «Будущее безопасности киберфизических систем в России. Перспективы и векторы развития»