Publications associées à From human action understanding to robot action execution: how the physical properties of handled objects modulate non-verbal cues

Publications associées (39)

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

Anne-Clémence Corminboeuf, Friedrich Eisenbrand, Rubén Laplaza Solanas, Puck Elisabeth van Gerwen, Ksenia Briling, Jan Weinreich, Matthieu Nicolas Haeberle

Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning (ML) applied to chemistry, we demonstrate the relev ...

IOP Publishing Ltd2025

On the added value of sequential deep learning for the upscaling of evapotranspiration

Devis Tuia

Estimating ecosystem-atmosphere fluxes such as evapotranspiration (ET) in a robust manner and at a global scale remains a challenge. Methods based on machine learning (ML) have shown promising results in achieving such upscaling, providing a complementary ...

2025

Koopman-based Data-driven Robust Control of Nonlinear Systems Using Integral Quadratic Constraints

Alireza Karimi, Mert Eyuboglu

This paper introduces a novel method for data-driven robust control of nonlinear systems based on the Koopman operator, utilizing Integral Quadratic Constraints (IQCs). The Koopman operator theory facilitates the linear representation of nonlinear system d ...

2024

Data-Driven Control Synthesis using Koopman Operator: A Robust Approach

Alireza Karimi, Nathan Russell Powell, Mert Eyuboglu

This paper proposes a data-driven control design method for nonlinear systems that builds upon the Koopman operator framework. In particular, the Koopman operator is used to lift the nonlinear dynamics to a higher-dimensional space where the so-called obse ...

2024

Non-invasive stimulation of the human striatum disrupts reinforcement learning of motor skills

Friedhelm Christoph Hummel, Takuya Morishita, Maximilian Jonas Wessel, Elena Beanato, Traian Popa, Pierre Theopistos Vassiliadis, Fabienne Windel, Esra Neufeld

Reinforcement feedback can improve motor learning, but the underlying brain mechanisms remain underexplored. In particular, the causal contribution of specific patterns of oscillatory activity within the human striatum is unknown. To address this question, ...

Nature Portfolio2024

Uncovering interactions between Descending Neurons as a functional principle of behavioural control

Jonas Friedrich Braun

Animals, including humans, exhibit a remarkable variety of complex behaviours. How the nervous system controls all these behaviours ranging from simple, stereotyped movements to flexible, adaptive actions is a central questions of neuroscience. One of the ...

EPFL2024

Frequency domain data-driven robust and optimal control

Philippe Louis Schuchert

The goal of this thesis is to propose pragmatic solutions to real challenges faced in the industry. The scope of this thesis encompasses two subjects: frequency-based structured controller synthesis for linear time-invariant (LTI) systems on one side, and ...

EPFL2024

Learning to Boost the Performance of Stable Nonlinear Systems

Giancarlo Ferrari Trecate, Luca Furieri, Clara Lucía Galimberti

The growing scale and complexity of safety-critical control systems underscore the need to evolve current control architectures aiming for the unparalleled performances achievable through state-of-the-art optimization and machine learning algorithms. Howev ...

2024

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Paul Scharnhorst

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

EPFL2024

Knowledge Distillation for Face Recognition Using Synthetic Data With Dynamic Latent Sampling

Hatef Otroshi Shahreza

State-of-the-art face recognition models are computationally expensive for mobile applications. Training lightweight face recognition models also requires large identity-labeled datasets, raising privacy and ethical concerns. Generating synthetic datasets ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2024

Eye-Rubbing Detection Using a Smartwatch: A Feasibility Study Demonstrated High Accuracy With Machine Learning

Alexandre Massoud Alahi, Tom Louis Mery

Purpose: In this work, we present a new machine learning method based on the transformer neural network to detect eye rubbing using a smartwatch in a real-life setting. In ophthalmology, the accurate detection and prevention of eye rubbing could reduce inc ...

2024

Live-cell imaging powered by computation

Suliana Manley

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preservi ...

Nature Portfolio2024

Experimental Validation of Model-less Robust Voltage Control using Measurement-based Estimated Voltage Sensitivity Coefficients

Mario Paolone, Rahul Kumar Gupta

Increasing adoption of smart meters and phasor measurement units (PMUs) in power distribution networks are enabling the adoption of data-driven/model-less control schemes to mitigate grid issues such as over/under voltages and power-flow congestions. Howev ...

IEEE2023

Data-driven models in fusion exhaust: AI methods and perspectives

Yoeri Renier Johannes Poels

A review is given on the highlights of a scatter-shot approach of developing machine-learning methods and artificial neural networks based fast predictors for the application to fusion exhaust. The aim is to enable and facilitate optimized and improved mod ...

IOP Publishing Ltd2024

Haptics based collaborative steering control in automated driving environments

Tomohiro Nakade

A vehicle's steering is a particular system in that it is exposed to individual subjective reviews based on criteria that are hard to assess quantitatively. Haptic design of such systems is a prime concern that has been at the center of industrial developm ...

EPFL2023

Memory of Motion for Initializing Optimization in Robotics

Teguh Santoso Lembono

Many robotics problems are formulated as optimization problems. However, most optimization solvers in robotics are locally optimal and the performance depends a lot on the initial guess. For challenging problems, the solver will often get stuck at poor loc ...

EPFL2022

Do Children Adapt Their Perspective to a Robot When They Fail to Complete a Task?

Pierre Dillenbourg, Elmira Yadollahi

Spatial understanding and communication are essential skills in human interaction. An adequate understanding of others' spatial perspectives can increase the quality of the interaction, both perceptually and cognitively. In this paper, we take the first st ...

Assoc Computing Machinery2022

Distributed neural network control with dependability guarantees: a compositional port-Hamiltonian approach

Giancarlo Ferrari Trecate, Luca Furieri, Muhammad Zakwan, Clara Lucía Galimberti

Large-scale cyber-physical systems require that control policies are distributed, that is, that they only rely on local real-time measurements and communication with neighboring agents. Optimal Distributed Control (ODC) problems are, however, highly intrac ...

2022

Foot-Interfaces Control and Human-Robot Interaction in Four-Handed Manipulation

Jacob Hernandez Sanchez

Humans have a great capacity to perform complex manipulations. However, there are many tasks for which two hands are not enough, such as in surgery. For such tasks, a dyad of people is required. However, when not used to work together, a dyad collaboration ...

EPFL2022

Machine-learning predictor of nerve fiber firing rate allows the automatic optimization of electrical stimulation protocols

Silvestro Micera, Simone Romeni, Gabriele Marino

Computational models have been widely employed to study the electrical stimulation of the nervous system. Still, most applications either study fundamental mechanisms underlying stimulation, or address qualitative scientific questions. When quantitative qu ...

IEEE2023