14 citations · 24 across the 8 of their papers we have counts for
9 papers
A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo
Miguel Vasco, Takuma Seno, Kenta Kawamoto +3
Racing autonomous cars faster than the best human drivers has been a longstanding grand challenge for the fields of Artificial Intelligence and robotics. Recently, an end-to-end de…
NeuralSolver: Learning Algorithms For Consistent and Efficient Extrapolation Across General Tasks
Bernardo Esteves, Miguel Vasco, Francisco S. Melo
We contribute NeuralSolver, a novel recurrent solver that can efficiently and consistently extrapolate, i.e., learn algorithms from smaller problems (in terms of observation size)…
Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning
Pedro P. Santos, Diogo S. Carvalho, Miguel Vasco +4
We introduce hybrid execution in multi-agent reinforcement learning (MARL), a new paradigm in which agents aim to successfully complete cooperative tasks with arbitrary communicati…
Perceive, Represent, Generate: Translating Multimodal Information to Robotic Motion Trajectories
Fábio Vital, Miguel Vasco, Alberto Sardinha +1
We present Perceive-Represent-Generate (PRG), a novel three-stage framework that maps perceptual information of different modalities (e.g., visual or sound), corresponding to a seq…
Geometric Multimodal Contrastive Representation Learning
Petra Poklukar, Miguel Vasco, Hang Yin +3
Learning representations of multimodal data that are both informative and robust to missing modalities at test time remains a challenging problem due to the inherent heterogeneity…
How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning Agents
Miguel Vasco, Hang Yin, Francisco S. Melo +1
This work addresses the problem of sensing the world: how to learn a multimodal representation of a reinforcement learning agent's environment that allows the execution of tasks un…
MHVAE: a Human-Inspired Deep Hierarchical Generative Model for Multimodal Representation Learning
Miguel Vasco, Francisco S. Melo, Ana Paiva
Humans are able to create rich representations of their external reality. Their internal representations allow for cross-modality inference, where available perceptions can induce…
Playing Games in the Dark: An approach for cross-modality transfer in reinforcement learning
Rui Silva, Miguel Vasco, Francisco S. Melo +2
In this work we explore the use of latent representations obtained from multiple input sensory modalities (such as images or sounds) in allowing an agent to learn and exploit polic…
Learning multimodal representations for sample-efficient recognition of human actions
Miguel Vasco, Francisco S. Melo, David Martins de Matos +2
Humans interact in rich and diverse ways with the environment. However, the representation of such behavior by artificial agents is often limited. In this work we present \textit{m…