Search Results for author: Fabio Ferreira

Found 11 papers, 7 papers with code

Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies

1 code implementation24 Jan 2021 Fabio Ferreira, Thomas Nierhoff, Frank Hutter

This work explores learning agent-agnostic synthetic environments (SEs) for Reinforcement Learning.

Acrobot

MDP Playground: Controlling Orthogonal Dimensions of Hardness in Toy Environments

no code implementations28 Sep 2020 Raghu Rajan, Jessica Lizeth Borja Diaz, Suresh Guttikonda, Fabio Ferreira, André Biedenkapp, Frank Hutter

We present MDP Playground, an efficient benchmark for Reinforcement Learning (RL) algorithms with various dimensions of hardness that can be controlled independently to challenge algorithms in different ways and to obtain varying degrees of hardness in generated environments.

OpenAI Gym

UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands

no code implementations24 Oct 2019 Lin Shao, Fabio Ferreira, Mikael Jorda, Varun Nambiar, Jianlan Luo, Eugen Solowjow, Juan Aparicio Ojea, Oussama Khatib, Jeannette Bohg

The majority of previous work has focused on developing grasp methods that generalize over novel object geometry but are specific to a certain robot hand.

Software Engineering Meets Deep Learning: A Mapping Study

no code implementations25 Sep 2019 Fabio Ferreira, Luciana Lourdes Silva, Marco Tulio Valente

Deep Learning (DL) is being used nowadays in many traditional Software Engineering (SE) problems and tasks.

MDP Playground: A Design and Debug Testbed for Reinforcement Learning

1 code implementation17 Sep 2019 Raghu Rajan, Jessica Lizeth Borja Diaz, Suresh Guttikonda, Fabio Ferreira, André Biedenkapp, Jan Ole von Hartz, Frank Hutter

We present \emph{MDP Playground}, an efficient testbed for Reinforcement Learning (RL) agents with \textit{orthogonal} dimensions that can be controlled independently to challenge agents in different ways and obtain varying degrees of hardness in generated environments.

OpenAI Gym

Learning Visual Dynamics Models of Rigid Objects using Relational Inductive Biases

1 code implementation9 Sep 2019 Fabio Ferreira, Lin Shao, Tamim Asfour, Jeannette Bohg

The first, Graph Networks (GN) based approach, considers explicitly defined edge attributes and not only does it consistently underperform an auto-encoder baseline that we modified to predict future states, our results indicate how different edge attributes can significantly influence the predictions.

Noise Regularization for Conditional Density Estimation

1 code implementation21 Jul 2019 Jonas Rothfuss, Fabio Ferreira, Simon Boehm, Simon Walther, Maxim Ulrich, Tamim Asfour, Andreas Krause

To address this issue, we develop a model-agnostic noise regularization method for CDE that adds random perturbations to the data during training.

Density Estimation

Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks

1 code implementation3 Mar 2019 Jonas Rothfuss, Fabio Ferreira, Simon Walther, Maxim Ulrich

Given a set of empirical observations, conditional density estimation aims to capture the statistical relationship between a conditional variable $\mathbf{x}$ and a dependent variable $\mathbf{y}$ by modeling their conditional probability $p(\mathbf{y}|\mathbf{x})$.

Density Estimation

Introducing the Simulated Flying Shapes and Simulated Planar Manipulator Datasets

2 code implementations2 Jul 2018 Fabio Ferreira, Jonas Rothfuss, Eren Erdal Aksoy, You Zhou, Tamim Asfour

We release two artificial datasets, Simulated Flying Shapes and Simulated Planar Manipulator that allow to test the learning ability of video processing systems.

Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution

1 code implementation12 Jan 2018 Jonas Rothfuss, Fabio Ferreira, Eren Erdal Aksoy, You Zhou, Tamim Asfour

We present a novel deep neural network architecture for representing robot experiences in an episodic-like memory which facilitates encoding, recalling, and predicting action experiences.

Cannot find the paper you are looking for? You can Submit a new open access paper.