6 citations · 12 across the 5 of their papers we have counts for
8 papers
Learning Synthetic Environments and Reward Networks for Reinforcement Learning
Fabio Ferreira, Thomas Nierhoff, Andreas Saelinger +1
We introduce Synthetic Environments (SEs) and Reward Networks (RNs), represented by neural networks, as proxy environment models for training Reinforcement Learning (RL) agents. We…
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019
Zhengying Liu, Adrien Pavao, Zhen Xu +22
This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) th…
Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies
Fabio Ferreira, Thomas Nierhoff, Frank Hutter
This work explores learning agent-agnostic synthetic environments (SEs) for Reinforcement Learning. SEs act as a proxy for target environments and allow agents to be trained more e…
Learning Visual Dynamics Models of Rigid Objects using Relational Inductive Biases
Fabio Ferreira, Lin Shao, Tamim Asfour +1
Endowing robots with human-like physical reasoning abilities remains challenging. We argue that existing methods often disregard spatio-temporal relations and by using Graph Neural…
UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands
Lin Shao, Fabio Ferreira, Mikael Jorda +6
To achieve a successful grasp, gripper attributes such as its geometry and kinematics play a role as important as the object geometry. The majority of previous work has focused on…
Noise Regularization for Conditional Density Estimation
Jonas Rothfuss, Fabio Ferreira, Simon Boehm +4
Modelling statistical relationships beyond the conditional mean is crucial in many settings. Conditional density estimation (CDE) aims to learn the full conditional probability den…