Tootfinder

End-to-End Low-Level Neural Control of an Industrial-Grade 6D Magnetic Levitation System
Philipp Hartmann, Jannick Strangh\"oner, Klaus Neumann
https://arxiv.org/abs/2509.01388

End-to-End Low-Level Neural Control of an Industrial-Grade 6D Magnetic Levitation System
Magnetic levitation is poised to revolutionize industrial automation by integrating flexible in-machine product transport and seamless manipulation. It is expected to become the standard drive for automated manufacturing. However, controlling such systems is inherently challenging due to their complex, unstable dynamics. Traditional control approaches, which rely on hand-crafted control engineering, typically yield robust but conservative solutions, with their performance closely tied to the ex…

Optimal area exploration by resetting active particles
Kristian St{\o}levik Olsen, Hartmut L\"owen, Lorenzo Caprini
https://arxiv.org/abs/2510.01087 https://

Optimal area exploration by resetting active particles
Identifying optimal strategies for efficient spatial exploration is crucial, both for animals seeking food and for robotic search processes, where maximizing the covered area is a fundamental requirement. Here, we propose position resetting as an optimal protocol to enhance spatial exploration in active matter systems. Specifically, we show that the area covered by an active Brownian particle exhibits a non-monotonic dependence on the resetting rate, demonstrating that resetting can optimize sp…

Time-energy tradeoff in stochastic resetting using optimal control
R\'emi Goerlich, Kristian St{\o}levik Olsen, Hartmut L\"owen, Yael Roichman
https://arxiv.org/abs/2507.19387

Time-energy tradeoff in stochastic resetting using optimal control
Stochastic resetting is a driving mechanism that is known to minimize the first passage time to reach a target, at the cost of energy expenditure. The choice of the physical implementation of each resetting event determines the tradeoff between the acceleration of the search process and its energetic cost. Here, we present an optimal transport protocol that balances the duration and the energetic cost of each resetting event. This protocol drives a harmonically trapped Brownian particle between…

Crosslisted article(s) found for q-bio.OT. https://arxiv.org/list/q-bio.OT/new
[1/1]:
- Neural cellular automata: applications to biology and beyond classical AI
Benedikt Hartl, Michael Levin, L\'eo Pio-Lopez