1 code implementation • 5 Nov 2024 • Farzaneh Taleb, Miguel Vasco, Antônio H. Ribeiro, Mårten Björkman, Danica Kragic
The human brain encodes stimuli from the environment into representations that form a sensory perception of the world.
no code implementations • 2 Oct 2024 • Alfredo Reichlin, Gustaf Tegnér, Miguel Vasco, Hang Yin, Mårten Björkman, Danica Kragic
Given a finite set of sample points, meta-learning algorithms aim to learn an optimal adaptation strategy for new, unseen tasks.
no code implementations • 18 Jun 2024 • Miguel Vasco, Takuma Seno, Kenta Kawamoto, Kaushik Subramanian, Peter R. Wurman, Peter Stone
Racing autonomous cars faster than the best human drivers has been a longstanding grand challenge for the fields of Artificial Intelligence and robotics.
no code implementations • 23 Feb 2024 • 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) and execute those algorithms in large problems.
no code implementations • 16 Feb 2024 • Alfredo Reichlin, Miguel Vasco, Hang Yin, Danica Kragic
We use the proposed value function to guide the learning of a policy in an actor-critic fashion, a method we name MetricRL.
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
+2
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.