Intelligent Soft Robotic System for Sensing and Recognition via Triboelectric‐Based Multi‐Sensor Fusion

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Humans and animals both use acoustic signals for vocal communication. The advent of self-supervised learning (SSL) has enabled neural networks to learn robust and general feature representations through the intrinsic acoustic structure of input signals, wi ...

Animals, including humans, interact with the external environment primarily through motion. Replicating their motor control skills in artificial embodied agents is a major objective of artificial intelligence research. This thesis presents a collection of ...

Tactile fingertips are a vital component of biological dexterity, where they convey information from the environment through our sensory systems. Similarly, sensorized robotic fingertips are needed to unlock robotic dexterity, versatility, and diverse inte ...

The dynamics of neuron populations commonly evolve on low-dimensional manifolds. Thus, we need methods that learn the dynamical processes over neural manifolds to infer interpretable and consistent latent representations. We introduce a representation lear ...

Interactive robotics is increasingly used to investigate animal behaviour, including communication, mating and collective dynamics. An essential component for such systems is sensing the animals and the relevant aspects of the habitat to enable coherent cl ...

Foundation models are highly versatile neural-network architectures capable of processing different data types, such as text and images, and generalizing across various tasks like classification and generation. Inspired by this success, we propose Foundati ...

While the accurate description of redox reactions remains a challenge for first-principles calculations, it has been shown that extended Hubbard functionals (DFT+U+V) can provide a reliable approach, mitigating self-interaction errors, in materials with st ...

The growing deployment of low-cost, distributed sensor networks in environmental and biomedical domains has enabled continuous, large-scale health monitoring. However, these systems often face challenges related to degraded data quality caused by sensor dr ...

Distributed optimization is the standard way of speeding up machine learning training, and most of the research in the area focuses on distributed first-order, gradient-based methods. Yet, there are settings where some computationally-bounded nodes may not ...

Machine learning models have made significant progress in load forecasting, but their forecast accuracy is limited in cases where historical load data is scarce. Inspired by the outstanding performance of large language models (LLMs) in computer vision and ...

Incorporating pixel contextual information is critical for accurate segmentation. In this paper, we show that an effective way to incorporate contextual information is through a patch-based classifier. This patch classifier is trained to identify classes p ...

This thesis investigates methods to construct more capable models more efficiently, focusing on two aspects: improved architectures and optimization. We examine principled architectural modifications that reduce computational costs or introduce features fo ...

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

Goal: In patients with coronary artery disease, the prediction of future cardiac events such as myocardial infarction (MI) remains a major challenge. In this work, we propose a novel anatomy-informed multimodal deep learning framework to predict future MI ...

Auditory research aims in general to lead to understanding of physiological processes. By contrast, the state of the art in automatic speech processing (notably recognition) is dominated by large pre-trained models that are meant to be used as black-boxes. ...

In recent years, soft robotics has surged in applications like wearables, drones, smart fabrics, and medical instruments. Due to their compliance, these devices excel in tasks demanding dexterity and adaptability, such as manipulation, locomotion, crash re ...

We present a chance-constrained model predictive control (MPC) framework under Gaussian mixture model (GMM) uncertainty. Specifically, we consider the uncertainty that arises from predicting future behaviors of moving obstacles, which may exhibit multiple ...

Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale ...

Polymer optical fibers (POFs) are an essential component of photonic textile sensors for the development of healthcare@home technologies. However, fabricating tailored POFs exhibiting the required properties for such applications remains challenging. Here, ...

Efficient information retrieval is a fundamental requirement in a wide array of applications, ranging from bioinformatics to database systems, and critically hinges on the complexity of an underlying distance metric. This work presents a novel approach to ...

Intelligent Soft Robotic System for Sensing and Recognition via Triboelectric‐Based Multi‐Sensor Fusion

Chattez avec Graph Search

Transferability of Learnt Speech Representations for Decoding Non-Human Vocal Communication

Musculoskeletal motor control with reinforcement learning

Automated Benchmarking of Variable‐Property Soft Robotic Fingertips to Enable Task‐Optimized Sensor Selection

MARBLE: interpretable representations of neural population dynamics using geometric deep learning

Extending Sensory Capabilities of a Biohybrid System: Prediction of Honeycomb Fill

Foundation neural-networks quantum states as a unified Ansatz for multiple hamiltonians

Teaching oxidation states to neural networks

Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Sensor Fusion

Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence

TimeGPT in load forecasting: A large time series model perspective

Beyond Pixels: Semi-supervised Semantic Segmentation with a Multi-scale Patch-Based Multi-label Classifier

Enhanced Architectures and Optimization Methods for Efficient Language Modeling

Topics in statistical physics of high-dimensional machine learning

Anatomy-Informed Multimodal Learning for Myocardial Infarction Prediction

Training a Filter-Based Model of the Cochlea in the Context of Pre-Trained Acoustic Models

Filiform Variable Stiffness Technologies for Medical Robotics

Recursively Feasible Chance-Constrained Model Predictive Control Under Gaussian Mixture Model Uncertainty

Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains

Microfluidic wet spinning of soft polydimethylsiloxane polymer optical fibers

Efficient Neural Clustering and Compression of Strings Through Approximate Euclidean Embeddings of the Levenshtein Distance