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Alessandro Forino

3 accepted papers

2026

Guided Multi-Fidelity Bayesian Optimization for Data-driven Controller Tuning with Digital Twins

RA-L 2026

We propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">guided multi-fidelity Bayesian optimization</i> framework for data-efficient controller tuning that integrates corrected digital twin simulations with real-world measurements. The method ta

Cited by 1SourceScholar
2026

Towards the Best Robot for the Job: Optimising Actuation Design through Multi-Task Co-Design and Component Selection

ICRA 2026poster

We propose a multi-task co-design approach to design a robot's actuation (motor sizes and gear ratios) based on trajectory optimisation. Leveraging an actuation model fit on data of series of components, we find the optimal set of design parameters for all joints over a set of representative tasks f…

Cited by 0Scholar