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David Leeftink

@davidleeftink.bsky.social
68 followers 118 following 11 posts

PhD student in Machine learning @ Donders Institute Nijmegen | Machine Learning, Control, Reinforcement Learning. davidleeftink.github.io

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Reposted by David Leeftink
Nico Bohlinger @nicobohlinger.bsky.social · 01/10/2026
⚡ One policy, millions of embodiments, over 200 robot models. Can we add yours? We're building γ₀, a generalist RL policy for motion control trained across millions of randomized embodiments derived from a growing collection of more than 200 robot models.
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Reposted by David Leeftink
Max Hinne @maxhinne.bsky.social · 29/09/2026
Bayesian analyses are great, but making the right model is very challenging. Come work with me on implementing efficient Bayesian multiverses, where we help researchers across empirical domains to improve their models! More information on the Radboud University website: www.ru.nl/en/working-a...
ru.nl
Research Assistant at the Donders Centre for Cognition: Computational Modelling and Scientific Software | Radboud University
Do you want to work as a Research Assistant at the Donders Centre for Cognition: Computational Modelling and Scientific Software? Check our vacancy!
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Reposted by David Leeftink
Kai Ploeger @kaiploeger.bsky.social · 24/06/2026
Learning five-ball juggling on the second attempt, with two Barrett WAMs. Most humans take years of practice. Paper and videos: kai-ploeger.com/residual-juggling #robotics #juggling
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Reposted by David Leeftink
marcelvangerven.bsky.social @marcelvangerven.bsky.social · 08/05/2026
Proud to share a new pre-print by @davidleeftink.bsky.social and with @maxhinne.bsky.social. We make a formal connection between the Pontryagin minimum principle and deep recurrent reinforcement learning, improving performance on challenging control tasks! arxiv.org/pdf/2605.05373
Recurrent Reinforcement Learning via Neural co-state policies. NCP s are regularized during training to mirror the optimality conditions implied by the minimum principle. By structuring the hidden states as representations of the underlying optimal control co-states, the read-out layer acts as a control-Hamiltonian minimizer.
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David Leeftink @davidleeftink.bsky.social · 31/03/2026
#BayesianOptimization #SemiconductorManufacturing #ProcessOptimization #MachineLearning #IndustrialAutomation
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David Leeftink @davidleeftink.bsky.social · 31/03/2026
Evaluated on industrial-quality wafers, BOLD autonomously discovers high-quality production-ready process configurations under limited measurements. The configurations yield expert-level mechanical die strength and improve production throughput by up to 34% via human-in-the-loop refinement.
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David Leeftink @davidleeftink.bsky.social · 31/03/2026
We developed 𝗕𝗢𝗟𝗗 (Bayesian Optimization for Laser Dicing), the first scalable automation method for laser processes, which bridges complex physical manufacturing with probabilistic machine learning.
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David Leeftink @davidleeftink.bsky.social · 31/03/2026
Adapting multi-pass laser dicing to new, multi-layered semiconductor wafers is a notoriously challenging problem. No scalable automation methods exist today, so parameter optimization relies on human experts, typically requiring weeks of costly manual tuning.
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David Leeftink @davidleeftink.bsky.social · 31/03/2026
Our latest paper, just accepted in the IFAC journal Control Engineering Practice (Special Issue on Bayesian Optimization), explores exactly this: 📄 Full paper (open access): www.sciencedirect.com/science/arti...
lnkd.in
LinkedIn
This link will take you to a page that’s not on LinkedIn
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David Leeftink @davidleeftink.bsky.social · 31/03/2026
How can Bayesian optimization enable the discovery of complex laser processes in semiconductor manufacturing?
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Reposted by David Leeftink
Clément Canonne @ccanonne.github.io · 06/03/2026
Leibniz, looking at the universe: "Why is there something instead of nothing?" Me, looking at my Outlook calendar: same
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Reposted by David Leeftink
Christian Wolf @chriswolfvision.bsky.social · 05/03/2026
It's time for this picture again ... #ECCV2026
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David Leeftink @davidleeftink.bsky.social · 01/12/2025
Congratulations, Gergely!
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Reposted by David Leeftink
Max Hinne @maxhinne.bsky.social · 01/12/2025
Proud to share a new pre-print by @davidleeftink.bsky.social and with @marcelvangerven.bsky.social on Bayesian optimization for automatic laser dicing in semiconductor manufacturing! arxiv.org/abs/2511.23141
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Reposted by David Leeftink
Matti Vuorre @matti.vuorre.com · 14/10/2025
Against Publishing: universonline.nl/nieuws/2025/... Preprints are read, shared, and cited, yet still dismissed as incomplete until blessed by a publisher. I argue that the true measure of scholarship lies in open exchange, not in the industry’s gatekeeping of what counts as published.
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David Leeftink @davidleeftink.bsky.social · 03/10/2025
I'd say yes! It allows new ideas to distribute faster, while remaining clear that it is not a reviewed article
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David Leeftink @davidleeftink.bsky.social · 04/09/2025
I'd like to!
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David Leeftink @davidleeftink.bsky.social · 03/09/2025
📄 Read the full paper: arxiv.org/abs/2504.02543 💻 Code available on GitHub: github.com/DavidLeeftin... A huge thank you to everyone who contributed to this work! #CDC2025 #ReinforcementLearning #OptimalControl #MachineLearning
arxiv.org
Optimal Control of Probabilistic Dynamics Models via Mean Hamiltonian Minimization
Without exact knowledge of the true system dynamics, optimal control of non-linear continuous-time systems requires careful treatment under epistemic uncertainty. In this work, we translate a probabil...
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David Leeftink @davidleeftink.bsky.social · 03/09/2025
Our recent paper “Optimal Control of Probabilistic Dynamics Models via Mean Hamiltonian Minimization,” has been accepted to CDC 2025! We demonstrate how applying optimal control principles can significantly improve planning in deep model-based reinforcement learning with epistemic uncertainty.
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