Publications associées à A Predictive Model for Tactile Force Estimation Using Audio-Tactile Data

Publications associées (16)

Learning by Teaching: Designing Teachable Agents to Support Children's Pronunciation Skill Learning

Pierre Dillenbourg, Chenyang Wang, Lucas Gérard Julien Burget

Pronunciation is a critical yet challenging aspect of language acquisition, especially for children. In this work, we propose a new teachable agent system designed to support children's pronunciation learning through a Learning-by-Teaching paradigm to addr ...

Association for Computing Machinery2025

“Data will solve robotics and automation: True or false?”: A debate

Aude Billard

Leading researchers debate the long-term influence of model-free methods that use large sets of demonstration data to train numerical generative models to control robots. ...

American Association for the Advancement of Science (AAAS)2025

Unified Human Localization and Trajectory Prediction with Monocular Vision

Alexandre Massoud Alahi, Yang Gao, Po-Chien Luan

Conventional human trajectory prediction models rely on clean curated data, requiring specialized equipment or manual labeling, which is often impractical for robotic applications. The existing predictors tend to overfit to clean observation affecting thei ...

IEEE2025

A Survey on Soft Robot Adaptability: Implementations, Applications, and Prospects [Survey]

Josephine Anna Eleanor Hughes, Qinghua Guan

Soft robots, compared to rigid robots, possess inherent advantages, including higher degrees of freedom, compliance, and enhanced safety, which have contributed to their increasing application across various fields. Among these benefits, adaptability is pa ...

2025

Efficient control of spider-like medical robots with capsule neural networks and modified spring search algorithm

Zaffar Ahmed Shaikh

This study introduces an innovative method for gesture recognition in medical robotics, utilizing Capsule Neural Networks (CNNs) in conjunction with the Modified Spring Search Algorithm (MSSA). This approach achieves remarkable efficiency in gesture identi ...

Nature Research2025

Drone-Based Urban Traffic Monitoring and User Behavior Prediction

Sohyeong Kim

As urban populations continue to expand and vehicle density increases, the need for effective traffic monitoring and management becomes increasingly critical. Urban areas face significant challenges such as growing congestion, safety concerns, and parking ...

EPFL2025

Robust Confinement State Classification with Uncertainty Quantification Through Ensembled Data-driven Methods

Olivier Sauter, Alessandro Pau, Yoeri Renier Johannes Poels, Cristina Venturini

Maximizing fusion performance in tokamaks relies on high energy confinement, often achieved through distinct operating regimes. The automated labeling of these confinement states is crucial to enable large-scale analyses or for real-time control applicatio ...

IOP Publishing Ltd2025

On-the-fly clustering for exascale molecular dynamics simulations

Jean-François Molinari

Computational resources have experienced exponential growth in the last decades enabling the simulation of complex physical problems at the cost of a massive increase in data storage. This is especially true for N-body simulations now reaching billions or ...

2025

On fundamental aspects of quantum extreme learning machines

Weijie Xiong, Mehrad Sahebi, Giorgio Facelli, Zoë Holmes, Thanasilp Supanut

Quantum extreme learning machines (QELMs) have emerged as a promising framework for quantum machine learning. Their appeal lies in the rich feature map induced by the dynamics of a quantum substrate-the quantum reservoir-and the efficient post-measurement ...

SPRINGERNATURE2025

Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs

Florent Gérard Krzakala, Bruno Loureiro, Ludovic Théo Stephan, Yatin Dandi, Luca Pesce, Luca Arnaboldi

We study the impact of the batch

n_b

on the iteration time

T

of training two-layer neural networks with one-pass stochastic gradient descent (SGD) on multi-index target functions of isotropic covariates. We characterize the optimal batch size minimizin ...

JMLR2024

Hierarchical Training of Deep Neural Networks Using Early Exiting

Pascal Frossard, Liza Carol Andrea Dunbar, Ahmet Caner Yüzügüler, Yamin Sepehri

Deep neural networks (DNNs) provide state-of-the-art accuracy for vision tasks, but they require significant resources for training. Thus, they are trained on cloud servers far from the edge devices that acquire the data. This issue increases communication ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Lab2Field transfer of a robotic raspberry harvester enabled by a soft sensorized physical twin

Josephine Anna Eleanor Hughes, Kai Christian Junge

Robotic fruit harvesting requires dexterity to handle delicate crops and development relying upon field testing possible only during the harvesting season. Here we focus on raspberry crops, and explore how the research methodology of harvesting robots can ...

2023

Automation in steep terrain agriculture: an optimal controller to prevent tipping and slipping of tethered robots on slopes

Josephine Anna Eleanor Hughes, Max Mirko Polzin

Autonomous robots have the potential to fundamentally transform conventional farming methods, e.g. by enabling economically viable farming of sloped arable land. However, navigation on slopes in harsh conditions is challenging for robots as they must be pr ...

TAYLOR & FRANCIS LTD2023

Forward-forward training of an optical neural network

Demetri Psaltis, Christophe Moser, Niyazi Ulas Dinç, Ilker Oguz, Mustafa Yildirim, Jih-Liang Hsieh

Neural networks (NNs) have demonstrated remarkable capabilities in various tasks, but their computation-intensive nature demands faster and more energy-efficient hardware implementations. Optics-based platforms, using technologies such as silicon photonics ...

Optica Publishing Group2023

AmadeusGPT: a natural language interface for interactive animal behavioral analysis

Alexander Mathis, Mackenzie Mathis, Shaokai Ye, Jessy Lauer, Mu Zhou

The process of quantifying and analyzing animal behavior involves translating the naturally occurring descriptive language of their actions into machine-readable code. Yet, codifying behavior analysis is often challenging without deep understanding of anim ...

2023

Dynamic Scheduling for Event-Driven Embedded Industrial Applications

Hossein Taji, David Atienza Alonso, Miguel Peon Quiros, José Angel Miranda Calero, Szabolcs Balási

This paper addresses the optimization of embedded platforms to meet the computing and real-time requirements of cyber-physical systems and IoT applications, including embedded intelligence. In this context, schedulers are vital in enhancing processor utili ...

New York2023