Control Theory

ROS developers and robotics researchers will exchange methods for building and deploying robot software. The programme combines technical talks, practical workshops, demonstrations and research-facility tours, with sessions on tactile sensing and cross-sensor learning, ROS 2 navigation, visual SLAM, humanoid teleoperation, impedance control for multi-robot manipulation, underwater simulation and industrial automation. Keynotes feature Lars Grimstad of Saga Robotics and Adam Norton of NERVE, whose session examines reproducing, integrating and benchmarking open-source grasping and manipulation systems. The three-day in-person event takes place at the Pollock Estate in Edinburgh. Tours and networking begin on 21 October; workshops start at 09:30 on 22 October; the main conference opens at 09:30 on 23 October and closes at 18:00. Times are Europe/London, BST (UTC+01:00). Late registration closes on 14 October: £390 professional or £130 student, including conference sessions, selected tours and workshops, refreshments and social events. Places and workshop choices are limited and allocated first come, first served. Book through the linked University of Edinburgh ePay page.

Raymundo Arróyave discusses alloy discovery guided by both metallurgical knowledge and Bayesian optimization. The approach combines physical models, high-throughput CALPHAD calculations, microstructural information and experimental feedback to search chemical and processing spaces that are difficult to explore through data alone. Examples involving high-entropy and refractory alloys address multiple objectives and constraints, including feasibility, correlated properties, uncertainty and microstructural sensitivity. The seminar then connects these decision methods to the Autonomous Robotic Metallurgist under development at his university. This platform links modular synthesis, characterization and testing with digital twins, human–robot collaboration and AI agents. The aim is a closed experimental loop in which scientific judgment and physical understanding guide automated execution.

SAB 2026 investigates the mechanisms that let natural and artificial agents adapt and survive in uncertain environments. Researchers compare biological behavior with robot experiments, computer simulations and mathematical models to evaluate principles and architectures of adaptive behavior. The programme covers collective intelligence, learning and skill acquisition, artificial perception and manipulation, and physical and social constraints on behavior. A cross-kingdom intelligence symposium includes Paco Calvo, Jens Krause, Vera Meyer and Ute Schmid. The meeting runs 19–22 October 2026 in Berlin, on CEST (Europe/Berlin, UTC+2). Workshops on 19 October at TU Berlin, Marchstraße 23, start at 09:00 following registration at 08:15; afternoon workshops end at 18:30. Topics include internal world models, genetic programming and homeostatic control. Conference sessions on 20–22 October take place at Universität der Künste, Georg-Neumann-Saal, Einsteinufer 43; sessions on 20 and 21 October begin at 09:00. Consult the detailed daily programme for session and venue changes. Public registration through Pretix is open. General admission is EUR 550, or EUR 450 for students with proof of status. Fees include sessions, coffee breaks and one networking dinner; lunches, other dinners and optional social activities are self-funded. No VAT is added.

The flagship conference of the IEEE Robotics and Automation Society, held May 24-28, 2027 at Coex in Seoul, South Korea, bringing together the world's top researchers and industry leaders to advance the field of robotics.

The human brain quickly adapts to a rapidly changing environment, often faster than what is thought to be possible by way of synaptic plasticity. Real-time control adaptation can only be achieved through faster processes such as neuromodulation. A number of neuromodulation models based on gain regulation have been proposed recently. But most of them utilize neuron-wide regulation and can not accommodate the simultaneous impact of different types of neurotransmitters on different parts of the dendritic tree. Models of synaptic gating and connectivity-tuning demonstrate that using enough additional parameters, one can mold a desired task into the network. Nevertheless, the relationship between the minimal amount of modulation and dimensionality of the task variation is not understood. Here, we address both of these questions using tractable representations of synaptic neuromodulation in recurrent neural networks (RNNs). Consistent with the experimental findings, modulation is performed by an external network of modulatory neurons, adapting their strengths and direction of modulation in response to the task variation. We introduce fixed synaptic masks, representing susceptibilities of the individual synapses to the neurotransmitter release from each modulatory neuron. These masks form a scaffold for neuromodulation patterns that can be applied to the network through activation of corresponding modulatory neurons. We observe that appropriate combination of masks can effectively mold network connectivity, adapting it to the changed environment. Specifically, we demonstrate how synaptic modulation can facilitate performance of the network on varying tasks of pattern discrimination and detection and link the (minimum) number of modulatory controls to the degrees of freedom in task variation. Our approach creates a useful framework for improving the mechanistic understanding of rapid adaptation in neural circuits. We shift the focus from synaptic plasticity as learning mechanism for one task to flexible neuromodulation that fits an entire subspace of tasks.

On Thursday February 15th, we will host Victoria Peterson and Julian Neumann. Victoria will tell us about “Spatial filtering to enhance signal processing in invasive neurophysiology”. Besides his scientific presentation on “Closed-loop deep brain stimulation as a neuroprosthetic of dopaminergic circuits – Current evidence and future opportunities”, Julian will give us a glimpse at the person behind the science. The talks will be followed by a shared discussion. Note: The talks will exceptionally be held at 10 ET / 4PM CET. You can register via talks.stimulatingbrains.org to receive the (free) Zoom link!

The Control and Automation group at inspire AG, an ETH Zurich strategic partner, offers a full-time postdoctoral position in Zurich with an industrial partner and ETH’s Automatic Control Laboratory. The researcher will develop iterative learning control and parameter-optimisation algorithms, connect theoretical analysis to tests on industrial hardware, and design adaptive control and estimation methods. The project aims to improve real-world industrial efficiency through learning-based control, process monitoring and practical guarantees. The advertised planned start is October 2026; the source labels the appointment permanent and specifies no application deadline. Applications are currently accepted according to the official vacancy instructions. Applicants send the listed documents to the employer with reference “ILC Postdoc”; the source page supplies the contact and document requirements.

Seminar by Morten Kjaergaard (Niels Bohr Institute, University of Copenhagen) on rapid FPGA-based feedback for superconducting qubits, including sparse-sampling techniques and on-FPGA inference enabling millisecond-timescale T1 estimation, ~100 ms readout optimization, and over 74,000 consecutive recalibrations in closed-loop operation.

Stephanie Gil studies reliable coordination in robot networks facing malicious information and ordinary environmental uncertainty. Communication signals provide physical evidence of trustworthiness that is difficult to forge; the cy-trust framework turns that evidence into probabilistic trust estimates and weights neighboring agents accordingly. For consensus and distributed optimization, the analysis establishes almost-sure convergence with bounded departures from nominal performance even when malicious agents form a majority of a node’s neighbors, exceeding the classical Byzantine threshold. Theory and hardware experiments support these guarantees. For natural uncertainty, real-time sensing is incorporated into rollout-based reinforcement learning, reweighting possible futures. Applications include fleet routing under random demand and Project CETI’s autonomous robotic rendezvous with sperm whales at sea. The talk closes with directions for combining trust and long-horizon sequential decisions to retain resilience when planning data may be corrupted.