Publications related to Learning Motion Reconstruction from Demonstration via Multi-Modal Soft Tactile Sensing

Related publications (20)

Highly agile flat swimming robot

Mehdi Benbedda, Herbert Shea, Karen Ann J Mulleners, Mrudhula Baskaran, Gaétan Irénée Auguste Anatole Armand Raynaud

Navigating and exploring the surfaces of bodies of water allow swimming robots to perform a range of measurements while efficiently communicating and harvesting energy from the Sun. Such environments are often highly unstructured and cluttered with plant m ...

AMER ASSOC ADVANCEMENT SCIENCE2025

Deep Architectural Form

Frederick Chando Kim

With recent innovations in artificial intelligence (AI), machines can now recognize architectural patterns and generate new forms in ways that far exceed human capacity. The integration of machines in the design process has ushered in a substantial shift i ...

EPFL2024

Tertiary Lymphoid Structures Generation Through Graph-Based Diffusion

Manuel Monteiro Lança Madeira, Pascal Frossard, Ntorina Thanou

Graph-based representation approaches have been proven to be successful in the analysis of biomedical data due to their capability of capturing intricate dependencies between biological entities, such as the spatial organization of different cell types in ...

Springer International Publishing Ag2024

Emergent coherent modes in nonlinear magnonic waveguides detected at ultrahigh frequency resolution

Dirk Grundler, Andrea Mucchietto, Mingran Xu, Kyongmo An

Nonlinearity of dynamic systems plays a key role in neuromorphic computing, which is expected to reduce the ever-increasing power consumption of machine learning and artificial intelligence applications. For spin waves (magnons), nonlinearity combined with ...

Nature Research2024

Perspective Aware Road Obstacle Detection

Pascal Fua, Mathieu Salzmann, Sina Honari, Krzysztof Maciej Lis

While road obstacle detection techniques have become increasingly effective, they typically ignore the fact that, in practice, the apparent size of the obstacles decreases as their distance to the vehicle increases. In this letter, we account for this by c ...

2023

Interpretable Representation Learning and Evaluation for Abstractive Summarization

Andreas Thomas Marfurt

Abstractive summarization has seen big improvements in recent years, mostly due to advances in neural language modeling, language model pretraining, and scaling models and datasets. While large language models generate summaries that are fluent, coherent, ...

EPFL2023

A benchmark dataset for machine learning in ecotoxicology

Kristin Schirmer, Fernando Perez Cruz

The use of machine learning for predicting ecotoxicological outcomes is promising, but underutilized. The curation of data with informative features requires both expertise in machine learning as well as a strong biological and ecotoxicological background, ...

Nature Portfolio2023

Explainability as fig leaf? An exploration of experts’ ethical expectations towards machine learning in psychiatry

Georg Michael Joachim Starke

The increasing implementation of programs supported by machine learning in medical contexts will affect psychiatry. It is crucial to accompany this development with careful ethical considerations informed by empirical research involving experts from the fi ...

2023

Discriminative clustering with representation learning with any ratio of labeled to unlabeled data

Corinne Louise Jones

We present a discriminative clustering approach in which the feature representation can be learned from data and moreover leverage labeled data. Representation learning can give a similarity-based clustering method the ability to automatically adapt to an ...

2022

Active origami platforms for human-robot interactions

Frédéric Henri Vaskin Giraud

Robots are employed to assist humans in lengthy, challenging, and repetitive tasks. However, the fields of rehabilitation, haptics, and assistive robotics have shown a significant need to support and interact with people in their everyday life. To facilita ...

EPFL2022

Safety Concerns Emerging from Robots Navigating in Crowded Pedestrian Areas

Aude Billard, Diego Felipe Paez Granados, Pericle Salvini

The slogan “robots will pervade our environment” has become a reality. Drones and ground robots are used for commercial purposes while semi-autonomous driving systems are standard accessories to traditional cars. However, while our eyes have been riveted o ...

2022

Measurement in the Age of Information

Marilyne Andersen, Forrest Simon Webler

Information is the resolution of uncertainty and manifests itself as patterns. Although complex, most observable phenomena are not random and instead are associated with deterministic, chaotic systems. The underlying patterns and symmetries expressed from ...

2022

The role of population receptive field size and recurrent processing in learning to “de-crowd”

Michael Herzog, Ayberk Ozkirli, Maya Anna Jastrzebowska

In crowding, the perception of a target is impeded by surrounding clutter. While traditional models are feedforward and local, there is increasing behavioral and neural evidence for a critical role of recurrent processing across the visual hierarchy in cro ...

SAGE Publishing2021

Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data

Martin Jaggi, Sebastian Urban Stich, Tao Lin, Sai Praneeth Reddy Karimireddy

Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization ch ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Adaptive Task-Space Force Control for Humanoid-to-Human Assistance

Anastasia Bolotnikova

We envision a humanoid robot to serve as a source of additional motion-support forces in assistance for frail persons. In this context, we present a control strategy for a humanoid to adaptively regulate its assistive force contribution. First, we identify ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Temporal analysis of multimodal data to predict collaborative learning outcomes

Kshitij Sharma, Jennifer Kaitlyn Olsen

The analysis of multiple data streams is a long-standing practice within educational research. Both multimodal data analysis and temporal analysis have been applied successfully, but in the area of collaborative learning, very few studies have investigated ...

WILEY2020

Engagement in Human-Agent Interaction: An Overview

Jauwairia Nasir, Catharine Regina Monika Maria Oertel Genannt Bierbach

Engagement is a concept of the utmost importance in human-computer interaction, not only for informing the design and implementation of interfaces, but also for enabling more sophisticated interfaces capable of adapting to users. While the notion of engage ...

2020

The diagnostic potential of multimodal neuroimaging measures in Parkinson's disease and atypical parkinsonism

Chang-Hyun Park

Introduction For the diagnosis of Parkinson's disease (PD) and atypical parkinsonism (AP) using neuroimaging, structural measures have been largely employed since structural abnormalities are most noticeable in the diseases. Functional abnormalities have b ...

2020

Bidirectional interactions facilitate the integration of a robot into a shoal of zebrafish Danio rerio

Francesco Mondada, Frank Bonnet, Vaios Papaspyros, Bertrand Eric Collignon

Many studies on collective animal behavior seek to identify the individual rules that underlie collective patterns. However, it was not until the recent advancements of micro-electronic and embedded systems that scientists were able to create mixed groups ...

2019

From standing balance to walking: A single control structure for a continuum of gaits

Auke Ijspeert, Salman Faraji, Seyed Hamed Razavi

This article presents a control algorithm framework with which a bipedal robot can perform a variety of gaits by only modifying a small set of control parameters. The controller drives a number of variables, called non-emergent variables, to their desired ...

2019