Publications associées à Contre-réaction

Augmenting locomotor perception by remapping tactile foot sensation to the back

Olaf Blanke, Mohamed Bouri, Oliver Alan Kannape, Atena Fadaeijouybari, Selim Jean Habiby Alaoui

Background :Sensory reafferents are crucial to correct our posture and movements, both reflexively and in a cognitively driven manner. They are also integral to developing and maintaining a sense of agency for our actions. In cases of compromised reafferen ...

BioMed Central2024

Stability Analysis of Input-Series/Output-Parallel Solid-State Transformers Equipped with Second-Order Harmonic Active Power Filters

Drazen Dujic, Andrea Cervone, Tianyu Wei

Solid-State Transformers with Input-Series/Output-Parallel configuration offer a convenient solution for AC/DC conversion due to their scalability and modularity. In this configuration, each module experiences a second-order harmonic ripple caused by local ...

2024

Integration of feedforward and feedback control in the neuromechanics of vertebrate locomotion: a review of experimental, simulation and robotic studies

Auke Ijspeert

Animal locomotion is the result of complex and multi-layered interactions between the nervous system, the musculo-skeletal system and the environment. Decoding the underlying mechanisms requires an integrative approach. Comparative experimental biology has ...

Company Biologists Ltd2023

Real-Time Localization for Closed-Loop Control of Assistive Furniture

Auke Ijspeert, Alexandre Massoud Alahi, Lixuan Tang, Anastasia Bolotnikova, George Adaimi, Chuanfang Ning

For people with limited mobility, navigating in cluttered indoor environment is challenging. In this work, we propose a mobile assistive furniture suite that is designed to ease the life of people with special needs in indoor movement. To enable intelligen ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Emilio Maddalena

This thesis is situated at the crossroads between machine learning and control engineering. Our contributions are both theoretical, through proposing a new uncertainty quantification methodology in a kernelized context; and experimental, through investigat ...

EPFL2023

A geometric optimal control approach for imitation and generalization of manipulation skills

Sylvain Calinon, Amirreza Razmjoo Fard, Jie Zhao

Daily manipulation tasks are characterized by regular features associated with the task structure, which can be described by multiple geometric primitives related to actions and object shapes. Only using Cartesian coordinate systems cannot fully represent ...

Elsevier2023

A Front-End CMOS Interface Circuit With High Voltage Charge Pump and Oscillator for Capacitive Micromachined Ultrasonic Transducers

Sandro Carrara, Gian Luca Barbruni, Zhengwen Jiang, Yihe Zhao

Label-free biosensors, combined with miniaturized micro-electromechanical sensory platforms, offer an attractive solution for real-time and facile monitoring of biomolecules due to their high sensitivity and selectivity without the need for specifically la ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Immediate effects of short-duration wellbeing practices on children’s handwriting and posture guided by a social robot

Pierre Dillenbourg, Chenyang Wang, Barbara Bruno, Daniel Carnieto Tozadore, Melike Cezayirlioglu

Handwriting practising, as any other repetitive task, often leads the practiser to an overconcentration state where their performance might be affected by postural and mental fatigue. Short breaks to perform unrelated activities, especially relaxation exer ...

2023

Making Computer Vision Models Robust and Adaptive

Shuqing Teresa Yeo

Visual perception is indispensable for many real-world applications. However, perception models deployed in the real world will encounter numerous and unpredictable distribution shifts, for example, changes in geographic locations, motion blur, and adverse ...

EPFL2023

Identifying important sensory feedback for learning locomotion skills

Auke Ijspeert, Guillaume Denis Antoine Bellegarda, Milad Shafiee Ashtiani

Robot motor skills can be acquired by deep reinforcement learning as neural networks to reflect state-action mapping. The selection of states has been demonstrated to be crucial for successful robot motor learning. However, because of the complexity of neu ...

NATURE PORTFOLIO2023