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ARXIV:2603.24138 · CONTROL SYSTEMS OPTIMIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.24138CONTROL SYSTEMS OPTIMIZATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALELukas Theiner · Maik Pfefferkorn · Yongpeng Zhao · Sebastian Hirt · Rolf Findeisen · arXiv
A framework for efficiently tuning control policies by combining numerical data with human preferences using multi-modal surrogate models.
Opportunity summary
Pain A framework for efficiently tuning control policies by combining numerical data with human preferences using multi-modal surrogate models.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A framework for efficiently tuning control policies by combining numerical data with human preferences using multi-modal surrogate models. Bayesian optimization provides a data-efficient framework for automating this process using numerical evaluations of an objective…
Tuning control policies manually to meet high-level objectives is often time-consuming. Bayesian optimization provides a data-efficient framework for automating this process using numerical evaluations of an objective function.
ScienceToStartup currently rates this 5.0/10 on the public viability pass. We illustrate the framework by tuning an autonomous vehicle's trajectory planner, showing that combining numerical and preference data significantly reduces the need for experiments…
Control Systems Optimization moved forward this cycle; last verified April 2026. Public score 5.0/10.
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A framework for efficiently tuning control policies by combining numerical data with human preferences using multi-modal surrogate models.
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10.48550/arXiv.2603.24138A framework for efficiently tuning control policies by combining numerical data with human preferences using multi-modal surrogate models.
Abstract
Tuning control policies manually to meet high-level objectives is often time-consuming. Bayesian optimization provides a data-efficient framework for automating this process using numerical evaluations of an objective function. However, many systems, particularly those involving humans, require optimization based on subjective criteria. Preferential Bayesian optimization addresses this by learning from pairwise comparisons instead of quantitative measurements, but relying solely on preference data can be inefficient. We propose a multi-fidelity, multi-modal Bayesian optimization framework that integrates low-fidelity numerical data with high-fidelity human preferences. Our approach employs Gaussian process surrogate models with both hierarchical, autoregressive and non-hierarchical, coregionalization-based structures, enabling efficient learning from mixed-modality data. We illustrate the framework by tuning an autonomous vehicle's trajectory planner, showing that combining numerical and preference data significantly reduces the need for experiments involving the human decision maker while effectively adapting driving style to individual preferences.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 5.0
PROBLEM
A framework for efficiently tuning control policies by combining numerical data with human preferences using multi-modal surrogate models. Bayesian optimization provides a data-efficient framework for automating this process using numerical evaluations of an objective function.
METHOD
Tuning control policies manually to meet high-level objectives is often time-consuming. Bayesian optimization provides a data-efficient framework for automating this process using numerical evaluations of an objective function.
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. We illustrate the framework by tuning an autonomous vehicle's trajectory planner, showing that combining numerical and preference data significantly reduces the need for experiments involving the human de...
WHY NOW
Control Systems Optimization moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A framework for efficiently tuning control policies by combining numerical data with human preferences using multi-modal surrogate models. Bayesian optimization provides a data-efficient framework for automating this process using numerical evaluations of an objective function.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Tuning control policies manually to meet high-level objectives is often time-consuming. Bayesian optimization provides a data-efficient framework for automating this process using numerical evaluations of an objective function.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. We illustrate the framework by tuning an autonomous vehicle's trajectory planner, showing that combining numerical and preference data significantly reduces the need for experiments involving the human decision maker while effectively adapting driving style to individual preferences.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Control Systems Optimization moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework for efficiently tuning control policies by combining numerical data with human preferences using multi-modal surrogate models.
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Control Systems Optimization
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5.0/10 public viability
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proof status
unverified
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