activity
20172021
most citedLanguage Bootstrapping: Learning Word Meanings From Perception-Action Association

38 citations · 40 across the 3 of their papers we have counts for

collaborators

10 papers

cs.CV2021

One-shot action recognition in challenging therapy scenarios

Alberto Sabater, Laura Santos, Jose Santos-Victor +3

One-shot action recognition aims to recognize new action categories from a single reference example, typically referred to as the anchor example. This work presents a novel approac…

cs.CV2019

Action-conditioned Benchmarking of Robotic Video Prediction Models: a Comparative Study

Manuel Serra Nunes, Atabak Dehban, Plinio Moreno +1

A defining characteristic of intelligent systems is the ability to make action decisions based on the anticipated outcomes. Video prediction systems have been demonstrated as a sol…

cs.RO2019

Action Anticipation for Collaborative Environments: The Impact of Contextual Information and Uncertainty-Based Prediction

Clebeson Canuto, Plinio Moreno, Jorge Samatelo +2

To interact with humans in collaborative environments, machines need to be able to predict (i.e., anticipate) future events, and execute actions in a timely manner. However, the ob…

cs.RO20192 cited

Learning Motor Resonance in Human-Human and Human-Robot Interaction with Coupled Dynamical System

Nuno Ferreira Duarte, Mirko Raković, José Santos-Victor

Human interaction involves very sophisticated non-verbal communication skills like understanding the goals and actions of others and coordinating our own actions accordingly. Neuro…

cs.RO2019

Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach

Jaeseok Kim, Nino Cauli, Pedro Vicente +4

Nowadays, autonomous service robots are becoming an important topic in robotic research. Differently from typical industrial scenarios, with highly controlled environments, service…

cs.CV2018

Applying Domain Randomization to Synthetic Data for Object Category Detection

João Borrego, Atabak Dehban, Rui Figueiredo +3

Recent advances in deep learning-based object detection techniques have revolutionized their applicability in several fields. However, since these methods rely on unwieldy and larg…