A Predictive Model for Tactile Force Estimation Using Audio-Tactile Data

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

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 ...

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. ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

A Predictive Model for Tactile Force Estimation Using Audio-Tactile Data

Chat with Graph Search

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

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

Unified Human Localization and Trajectory Prediction with Monocular Vision

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

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

Drone-Based Urban Traffic Monitoring and User Behavior Prediction

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

On fundamental aspects of quantum extreme learning machines

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

Hierarchical Training of Deep Neural Networks Using Early Exiting

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

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

AmadeusGPT: a natural language interface for interactive animal behavioral analysis

Dynamic Scheduling for Event-Driven Embedded Industrial Applications