1 code implementation • 23 Feb 2024 • Bernardo Esteves, Miguel Vasco, Francisco S. Melo
To address this gap, we propose NeuralThink, a new recurrent architecture that can consistently extrapolate to both symmetrical and asymmetrical tasks, where the dimensionality of the input and output are different.
no code implementations • 16 Feb 2024 • Alfredo Reichlin, Miguel Vasco, Hang Yin, Danica Kragic
Experimentally, we show how our method consistently outperforms other offline RL baselines in learning from sub-optimal offline datasets.
1 code implementation • 12 Oct 2022 • Pedro P. Santos, Diogo S. Carvalho, Miguel Vasco, Alberto Sardinha, Pedro A. Santos, Ana Paiva, Francisco S. Melo
We introduce hybrid execution in multi-agent reinforcement learning (MARL), a new paradigm in which agents aim to successfully complete cooperative tasks with arbitrary communication levels at execution time by taking advantage of information-sharing among the agents.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 6 Apr 2022 • Fábio Vital, Miguel Vasco, Alberto Sardinha, Francisco Melo
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 sequence of instructions, to an adequate sequence of movements to be executed by a robot.
1 code implementation • 7 Feb 2022 • Petra Poklukar, Miguel Vasco, Hang Yin, Francisco S. Melo, Ana Paiva, Danica Kragic
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 of data obtained from different channels.
1 code implementation • 7 Oct 2021 • Miguel Vasco, Hang Yin, Francisco S. Melo, Ana Paiva
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 under incomplete perceptual conditions.
1 code implementation • 6 Nov 2020 • Silvia Tulli, Marta Couto, Miguel Vasco, Elmira Yadollahi, Francisco Melo, Ana Paiva
In the application scenario, the child and the robot learn together how to play a zero-sum game that requires logical and mathematical thinking.
no code implementations • 4 Jun 2020 • Miguel Vasco, Francisco S. Melo, Ana Paiva
Humans are able to create rich representations of their external reality.
1 code implementation • 28 Nov 2019 • Rui Silva, Miguel Vasco, Francisco S. Melo, Ana Paiva, Manuela Veloso
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 policies over different subsets of input modalities.
no code implementations • 6 Mar 2019 • Miguel Vasco, Francisco S. Melo, David Martins de Matos, Ana Paiva, Tetsunari Inamura
In this work we present \textit{motion concepts}, a novel multimodal representation of human actions in a household environment.