Publications related to Slip Anticipation for Grasping Deformable Objects Using a Soft Force Sensor

Related publications (11)

Edge-Cloud Collaborated Object Detection via Bandwidth Adaptive Difficult-Case Discriminator

Object detection, a fundamental task in computer vision, is crucial for various intelligent edge computing applications. However, object detection algorithms are usually heavy in computation, hindering their deployments on resource-constrained edge devices ...

2025

GRADE: Generating Realistic and Dynamic Environments for robotics research with Isaac Sim

Chenghao Xu

Photorealistic synthetic data and novel rendering techniques significantly advanced computer vision research. However, datasets focused on computer vision applications cannot be easily applied to robotics because they typically lack physics-related informa ...

SAGE Publications2025

DATA-DRIVEN INVERSE DESIGN METHOD FOR TURBOMACHINERY

Fernando Perez Cruz, Luis Salamanca Miño, Firat Ozdemir

A data-driven inverse design method based on neural networks is proposed for turbomachinery. In the devised methodology, design parameters are provided as input to the neural network, and performance attributes, e.g. efficiency, as output. Once trained, th ...

American Society of Mechanical Engineers (ASME)2024

Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios

Olga Fink, Gaëtan Michel Frusque

The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods has shown significant efficacy in industrial settings, facilitating the transfer of operational experience and fault signatures between different operating conditions, di ...

2023

Advances and Open Problems in Federated Learning

Martin Jaggi, Ayfer Özgür Aydin, Sebastian Urban Stich, Florian Tramèr, Lie He

Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decen ...

NOW PUBLISHERS INC2021

From Human-Designed Convolutional Neural Networks Towards Robust Neural Architecture Search

Kaicheng Yu

Artificial intelligence has been an ultimate design goal since the inception of computers decades ago. Among the many attempts towards general artificial intelligence, modern machine learning successfully tackles many complex problems thanks to the progres ...

EPFL2021

Unsupervised Domain Adaptation for Cross-Subject Few-Shot Neurological Symptom Detection

Mahsa Shoaran, Bingzhao Zhu

Modern machine learning tools have shown promise in detecting symptoms of neurological disorders. However, current approaches typically train a unique classifier for each subject. This subject-specific training scheme requires long labeled recordings from ...

IEEE2021

On Vacuous and Non-Vacuous Generalization Bounds for Deep Neural Networks

Konstantinos Pitas

Deep neural networks have been empirically successful in a variety of tasks, however their theoretical understanding is still poor. In particular, modern deep neural networks have many more parameters than training data. Thus, in principle they should over ...

EPFL2020

A new spatial count data model with Bayesian additive regression trees for accident hot spot identification

Rico Krüger

The identification of accident hot spots is a central task of road safety management. Bayesian count data models have emerged as the workhorse method for producing probabilistic rankings of hazardous sites in road networks. Typically, these methods assume ...

2020

Theory of representation learning in cortical neural networks

Carlos Stein Naves de Brito

Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same f ...

EPFL2016

The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Daniel Roggen, Hesam Sagha

There is a growing interest on using ambient and wearable sensors for human activity recognition, fostered by several application domains and wider availability of sensing technologies. This has triggered increasing attention on the development of robust m ...

Elsevier2013