Adapting Neural Robot Dynamics on the Fly for Predictive Control

Fuente: arXiv
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Autori principali: Altawaitan, Abdullah, Atanasov, Nikolay
Natura: Preprint
Pubblicazione: 2026
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author Altawaitan, Abdullah
Atanasov, Nikolay
author_facet Altawaitan, Abdullah
Atanasov, Nikolay
contents Accurate dynamics models are critical for the design of predictive controller for autonomous mobile robots. Physics-based models are often too simple to capture relevant real-world effects, while data-driven models are data-intensive and slow to train. We introduce an approach for fast adaptation of neural robot dynamic models that combines offline training with efficient online updates. Our approach learns an incremental neural dynamics model offline and performs low-rank second-order parameter adaptation online, enabling rapid updates without full retraining. We demonstrate the approach on a real quadrotor robot, achieving robust predictive tracking control in novel operational conditions.
format Preprint
id arxiv_https___arxiv_org_abs_2604_04039
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Adapting Neural Robot Dynamics on the Fly for Predictive Control
Altawaitan, Abdullah
Atanasov, Nikolay
Robotics
Accurate dynamics models are critical for the design of predictive controller for autonomous mobile robots. Physics-based models are often too simple to capture relevant real-world effects, while data-driven models are data-intensive and slow to train. We introduce an approach for fast adaptation of neural robot dynamic models that combines offline training with efficient online updates. Our approach learns an incremental neural dynamics model offline and performs low-rank second-order parameter adaptation online, enabling rapid updates without full retraining. We demonstrate the approach on a real quadrotor robot, achieving robust predictive tracking control in novel operational conditions.
title Adapting Neural Robot Dynamics on the Fly for Predictive Control
topic Robotics
url https://arxiv.org/abs/2604.04039