32 citations · 112 across the 31 of their papers we have counts for
33 papers
All by Myself: Learning Individualized Competitive Behaviour with a Contrastive Reinforcement Learning optimization
Pablo Barros, Alessandra Sciutti
In a competitive game scenario, a set of agents have to learn decisions that maximize their goals and minimize their adversaries' goals at the same time. Besides dealing with the i…
I am Only Happy When There is Light: The Impact of Environmental Changes on Affective Facial Expressions Recognition
Doreen Jirak, Alessandra Sciutti, Pablo Barros +1
Human-robot interaction (HRI) benefits greatly from advances in the machine learning field as it allows researchers to employ high-performance models for perceptual tasks like dete…
Incorporating Rivalry in Reinforcement Learning for a Competitive Game
Pablo Barros, Ozge Nilay Yalcın, Ana Tanevska +1
Recent advances in reinforcement learning with social agents have allowed such models to achieve human-level performance on specific interaction tasks. However, most interactive sc…
CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition
Pablo Barros, Alessandra Sciutti
Current facial expression recognition systems demand an expensive re-training routine when deployed to different scenarios than they were trained for. Biasing them towards learning…
I Only Have Eyes for You: The Impact of Masks On Convolutional-Based Facial Expression Recognition
Pablo Barros, Alessandra Sciutti
The current COVID-19 pandemic has shown us that we are still facing unpredictable challenges in our society. The necessary constrain on social interactions affected heavily how we…
A Sub-Layered Hierarchical Pyramidal Neural Architecture for Facial Expression Recognition
Henrique Siqueira, Pablo Barros, Sven Magg +2
In domains where computational resources and labeled data are limited, such as in robotics, deep networks with millions of weights might not be the optimal solution. In this paper,…
Disambiguating Affective Stimulus Associations for Robot Perception and Dialogue
Henrique Siqueira, Alexander Sutherland, Pablo Barros +3
Effectively recognising and applying emotions to interactions is a highly desirable trait for social robots. Implicitly understanding how subjects experience different kinds of act…
An Ensemble with Shared Representations Based on Convolutional Networks for Continually Learning Facial Expressions
Henrique Siqueira, Pablo Barros, Sven Magg +1
Social robots able to continually learn facial expressions could progressively improve their emotion recognition capability towards people interacting with them. Semi-supervised le…
Incorporating Rivalry in Reinforcement Learning for a Competitive Game
Pablo Barros, Ana Tanevska, Ozge Yalcin +1
Recent advances in reinforcement learning with social agents have allowed us to achieve human-level performance on some interaction tasks. However, most interactive scenarios do no…
The FaceChannelS: Strike of the Sequences for the AffWild 2 Challenge
Pablo Barros, Alessandra Sciutti
Predicting affective information from human faces became a popular task for most of the machine learning community in the past years. The development of immense and dense deep neur…
Affect-Driven Modelling of Robot Personality for Collaborative Human-Robot Interactions
Nikhil Churamani, Pablo Barros, Hatice Gunes +1
Collaborative interactions require social robots to adapt to the dynamics of human affective behaviour. Yet, current approaches for affective behaviour generation in robots focus o…
The FaceChannel: A Fast & Furious Deep Neural Network for Facial Expression Recognition
Pablo Barros, Nikhil Churamani, Alessandra Sciutti
Current state-of-the-art models for automatic Facial Expression Recognition (FER) are based on very deep neural networks that are effective but rather expensive to train. Given the…
Analysis of Social Robotic Navigation approaches: CNN Encoder and Incremental Learning as an alternative to Deep Reinforcement Learning
Janderson Ferreira, Agostinho A. F. Júnior, Letícia Castro +3
Dealing with social tasks in robotic scenarios is difficult, as having humans in the learning loop is incompatible with most of the state-of-the-art machine learning algorithms. Th…
Performance Improvement of Path Planning algorithms with Deep Learning Encoder Model
Janderson Ferreira, Agostinho A. F. Júnior, Yves M. Galvão +3
Currently, path planning algorithms are used in many daily tasks. They are relevant to find the best route in traffic and make autonomous robots able to navigate. The use of path p…
Moody Learners -- Explaining Competitive Behaviour of Reinforcement Learning Agents
Pablo Barros, Ana Tanevska, Francisco Cruz +1
Designing the decision-making processes of artificial agents that are involved in competitive interactions is a challenging task. In a competitive scenario, the agent does not only…
Facial Expression Editing with Continuous Emotion Labels
Alexandra Lindt, Pablo Barros, Henrique Siqueira +1
Recently deep generative models have achieved impressive results in the field of automated facial expression editing. However, the approaches presented so far presume a discrete re…
The FaceChannel: A Light-weight Deep Neural Network for Facial Expression Recognition
Pablo Barros, Nikhil Churamani, Alessandra Sciutti
Current state-of-the-art models for automatic FER are based on very deep neural networks that are difficult to train. This makes it challenging to adapt these models to changing co…
CNN Encoder to Reduce the Dimensionality of Data Image for Motion Planning
Janderson Ferreira, Agostinho A. F. Júnior, Yves M. Galvão +2
Many real-world applications need path planning algorithms to solve tasks in different areas, such as social applications, autonomous cars, and tracking activities. And most import…
Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card Game
Pablo Barros, Ana Tanevska, Alessandra Sciutti
Learning how to adapt to complex and dynamic environments is one of the most important factors that contribute to our intelligence. Endowing artificial agents with this ability is…
iCub: Learning Emotion Expressions using Human Reward
Nikhil Churamani, Francisco Cruz, Sascha Griffiths +1
The purpose of the present study is to learn emotion expression representations for artificial agents using reward shaping mechanisms. The approach takes inspiration from the TAMER…
The Chef's Hat Simulation Environment for Reinforcement-Learning-Based Agents
Pablo Barros, Anne C. Bloem, Inge M. Hootsmans +4
To achieve social interactions within Human-Robot Interaction (HRI) environments is a very challenging task. Most of the current research focuses on Wizard-of-Oz approaches, which…
It's Food Fight! Introducing the Chef's Hat Card Game for Affective-Aware HRI
Pablo Barros, Alessandra Sciutti, Anne C. Bloem +4
Emotional expressions and their changes during an interaction affect heavily how we perceive and behave towards other persons. To design an HRI scenario that makes possible to obse…
What can computational models learn from human selective attention? A review from an audiovisual crossmodal perspective
Di Fu, Cornelius Weber, Guochun Yang +6
Selective attention plays an essential role in information acquisition and utilization from the environment. In the past 50 years, research on selective attention has been a centra…
The OMG-Empathy Dataset: Evaluating the Impact of Affective Behavior in Storytelling
Pablo Barros, Nikhil Churamani, Angelica Lim +1
Processing human affective behavior is important for developing intelligent agents that interact with humans in complex interaction scenarios. A large number of current approaches…