Publications associées à Editorial: Advances in robots for learning

Publications associées (11)

RPF-Search: Field-Based Search for Robot Person Following in Unknown Dynamic Environments

Autonomous robot person-following (RPF) systems are crucial for personal assistance and security but suffer from target loss due to occlusions in dynamic, unknown environments. Current methods rely on prebuilt maps and assume static environments, limiting ...

2025

FADE: Forecasting for anomaly detection on ECG

David Atienza Alonso, Denisa-Andreea Constantinescu

Background and Objective: Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to improve patient outcomes. Taking advantage of advances in machine learning and deep learning, multiple app ...

Elsevier BV2025

Discovering meaningful units from text sequences

Melika Behjati

In recent years, the field of Natural Language Processing has seen significant revolution by the introduction of Transformers, a stack of multiple layers of attention and non-linearity, capable of performing almost any task and the backbone for large found ...

EPFL2024

Data-Driven Kinematic Modeling of Physical Origami Robots

Jamie Paik, Mustafa Mete, Alexander Michael Schüssler

Origami-inspired structures facilitate the design of compliant and compact robots. However, physical origami robots possess inherent material compliance and mechanical imperfections, presenting challenges in modeling and redundant actuation for accurate co ...

2024

Online Learning of Continuous Signed Distance Fields Using Piecewise Polynomials

Ante Maric, Yiming Li, Sylvain Calinon

Reasoning about distance is indispensable for establishing or avoiding contact in manipulation tasks. To this end, we present an online approach for learning implicit representations of signed distance using piecewise polynomial basis functions. Starting f ...

Ieee-Inst Electrical Electronics Engineers Inc2024

3D deep learning for enhanced atom probe tomography analysis of nanoscale microstructures

Ye Wei

Quantitative analysis of microstructural features on the nanoscale, including precipitates, local chemical orderings (LCOs) or structural defects (e.g. stacking faults) plays a pivotal role in understanding the mechanical and physical responses of engineer ...

2024

Who's Helping Who? When Students Use ChatGPT to Engage in Practice Lab Sessions

Jérôme Guillaume Brender, Francesco Mondada, Laila Abdelsalam El-Hamamsy, Engin Walter Bumbacher

Little is understood about how chatbots powered by Large Language Models (LLMs) impact teaching and learning, and how to effectively integrate them into educational practices. This study examined whether and how using ChatGPT in a graduate-level robotics c ...

Springer Nature Switzerland2024

How Close are Predictive Models to Teachers in Detecting Learners at Risk?

Tanja Christina Käser Jacober

Detecting learners in need of support is a complex process for both teachers and machines. Most prior work has devised visualization tools that allow teachers to do so by analyzing educational indicators. Other recent efforts have been devoted to models th ...

ASSOC COMPUTING MACHINERY2023

Explainable ensemble learning model for predicting steel section-concrete bond strength

Xianlin Wang

This study evaluates the efficiency of an explainable ensemble learning framework in precisely predicting the bond strength between steel sections with different surface treatments and various concrete types. Besides seven numerical features, two categoric ...

2022

The neural resource allocation problem when enhancing human bodies with extra robotic limbs

Silvestro Micera, Solaiman Shokur, Giulia Dominijanni

The emergence of robotic body augmentation provides exciting innovations that will revolutionize the fields of robotics, human-machine interaction and wearable electronics. Although augmentative devices such as extra robotic arms and fingers are informed b ...

NATURE PORTFOLIO2021

Unsupervised Controllable Generation with Self-Training

Grigorios Chrysos

Recent generative adversarial networks (GANs) are able to generate impressive photo-realistic images. However, controllable generation with GANs remains an open research problem. Achieving controllable generation requires semantically interpretable and dis ...

IEEE2021