Publications associées à Toward more naturalistic tactile sensors

Publications associées (14)

Quantifying the biomimicry gap in biohybrid robot-fish pairs

Biohybrid systems in which robotic lures interact with animals have become compelling tools for probing and identifying the mechanisms underlying collective animal behavior. One key challenge lies in the transfer of social interaction models from simulatio ...

2024

Plasticity of sensory representations in the posterior insular cortex during fear learning

Denys Osypenko

Aversively-motivated associative learning allows animals to avoid harm and thus ensures survival. Aversive learning can be studied by the fear learning paradigm, in which an innocuous sensory stimulus like a tone (conditioned stimulus, CS), acquires a nega ...

EPFL2023

3D printing of resilient biogels for omnidirectional and exteroceptive soft actuators

Florian Hartmann

Soft robotics greatly benefits from nature as a source of inspiration, introducing innate means of safe interaction between robotic appliances and living organisms. In contrast, the materials involved are often nonbiodegradable or stem from nonrenewable re ...

2022

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

Sensing, Actuating, and Interacting Through Passive Body Dynamics: A Framework for Soft Robotic Hand Design

Josephine Anna Eleanor Hughes, Kieran Daniel Gilday

Mary Ann Liebert Inc.2022

Time-encoded multiplication-free spiking neural networks: application to data classification tasks

Ana Stanojevic

Spiking neural networks (SNNs) are mimicking computationally powerful biologically inspired models in which neurons communicate through sequences of spikes, regarded here as sparse binary sequences of zeros and ones. In neuroscience it is conjectured that ...

2022

Learning Optimal Impedance Control During Complex 3D Arm Movements

Sylvain Calinon

In daily life, humans use their limbs to perform various movements to interact with an external environment. Thanks to limb's variable and adaptive stiffness, humans can adapt their movements to unstable dynamics of the external environments. The underlyin ...

IEEE2021

Cell Type-Specific Membrane Potential Changes in Dorsolateral Striatum Accompanying Reward-Based Sensorimotor Learning

Tanya C Sippy, Carl Petersen, Sylvain Crochet

The striatum integrates sensorimotor and motivational signals, likely playing a key role in reward-based learning of goal-directed behavior. However, cell type-specific mechanisms underlying reinforcement learning remain to be precisely determined. Here, w ...

OXFORD UNIV PRESS2021

Global and high-level effects in crowding cannot be predicted by either high-dimensional pooling or target cueing

Michael Herzog, Adrien Christophe Doerig, Mauro Manassi, Alban Bornet, Oh-Hyeon Choung

In visual crowding, the perception of a target deteriorates in the presence of nearby flankers. Traditionally, target-flanker interactions have been considered as local, mostly deleterious, low-level, and feature specific, occurring when information is poo ...

Association for Research in Vision and Ophthalmology (ARVO)2021

Robot skills learning with Riemannian manifolds: Leveraging geometry-awareness in robot learning, optimization and control

Noémie Laure Gwendoline Jaquier

Humans exhibit outstanding learning and adaptation capabilities while performing various types of manipulation tasks. When learning new skills, humans are able to extract important information by observing examples of a task and efficiently refine a priori ...

EPFL2020

Towards BCI-Based Interfaces for Augmented Reality: Feasibility, Design and Evaluation

Camille Marie Jeunet, Nicolas Julien Roussel

Brain-Computer Interfaces (BCIs) enable users to interact with computers without any dedicated movement, bringing new hands-free interaction paradigms. In this paper we study the combination of BCI and Augmented Reality (AR). We first tested the feasibilit ...

2020

From High-Level to Low-Level Robot Learning of Complex Tasks: Leveraging Priors, Metrics and Dynamical Systems

Nadia Barbara Figueroa Fernandez

Humans have a remarkable way of learning, adapting and mastering new manipulation tasks. With the current advances in Machine Learning (ML), the promise of having robots with such capabilities seems to be on the cusp of reality. Transferring human-level sk ...

EPFL2019

Neuronal Correlates of Tactile Working Memory in Prefrontal and Vibrissal Somatosensory Cortex

Vahid Esmaeili

Tactile working memory engages a broad network of cortical regions in primates. To assess whether the conclusions drawn from primates apply to rodents, we examined the vibrissal primary somatosensory cortex (vS1) and the prelimbic cortex (PL) in a delayed ...

2019

A Robotic System for Adaptive Training and Function Assessment of Forelimb Retraction in Mice

Silvestro Micera

Rodent models are decisive for translational research in healthy and pathological conditions of motor function thanks to specific similarities with humans. Here, we present an upgraded version of the M-Platform, a robotic device previously designed to trai ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018