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Meta-learning framework accelerates control system adaptation with limited data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel meta-learning framework for designing optimal controllers for uncertain nonlinear systems, particularly when target system data is scarce. This approach leverages offline data from similar source systems to accelerate training and improve control performance in an online adaptation phase. The framework is formulated as a bi-level optimization problem and can integrate various learning algorithms, including neural state-space models and deep Q-networks, demonstrating enhanced performance over baseline methods in simulations and hardware experiments. AI

IMPACT This research could enable more efficient and effective control systems in scenarios with limited data, potentially impacting robotics and autonomous systems.

RANK_REASON The cluster contains an academic paper detailing a new research methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Meta-learning framework accelerates control system adaptation with limited data

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The cluster contains an academic paper detailing a new research methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Alisa Rupenyan ·

    Meta-Learning for Rapid Adaptation in Reference Tracking of Uncertain Nonlinear Systems

    In this paper, we address the problem of reference tracking for uncertain nonlinear systems. Since collecting data from the target system (i.e., the system of interest) is often challenging, our objective is to design optimal controllers using limited target system data. Meta-lea…

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