Search Results for author:
Found 13 papers, 6 papers with code
Planning the path with Reinforcement Learning: Optimal Robot Motion Planning in RoboCup Small Size League Environments
This work investigates the potential of Reinforcement Learning (RL) to tackle robot motion planning challenges in the dynamic RoboCup Small Size League (SSL).
Topological Semantic Mapping by Consolidation of Deep Visual Features
In contrast, this work introduces a topological semantic mapping method that uses deep visual features extracted by a CNN (GoogLeNet), from 2D images captured in multiple views of the environment as the robot operates, to create, through averages, consolidated representations of the visual features acquired in the regions covered by each topological node.
rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot Soccer
Reinforcement learning is an active research area with a vast number of applications in robotics, and the RoboCup competition is an interesting environment for studying and evaluating reinforcement learning methods.
An analysis of Reinforcement Learning applied to Coach task in IEEE Very Small Size Soccer
The IEEE Very Small Size Soccer (VSSS) is a robot soccer competition in which two teams of three small robots play against each other.
A Framework for Studying Reinforcement Learning and Sim-to-Real in Robot Soccer
This article introduces an open framework, called VSSS-RL, for studying Reinforcement Learning (RL) and sim-to-real in robot soccer, focusing on the IEEE Very Small Size Soccer (VSSS) league.
Deep Categorization with Semi-Supervised Self-Organizing Maps
The results show that Batch SS-SOM is a good option for semi-supervised classification and clustering.
Learning to Play Soccer by Reinforcement and Applying Sim-to-Real to Compete in the Real World
This work presents an application of Reinforcement Learning (RL) for the complete control of real soccer robots of the IEEE Very Small Size Soccer (VSSS), a traditional league in the Latin American Robotics Competition (LARC).
MOEA/D with Uniformly Randomly Adaptive Weights
In this paper, we propose the MOEA/D with Uniformly Randomly Adaptive Weights (MOEA/DURAW) which uses the Uniformly Randomly method as an approach to subproblems generation, allowing a flexible population size even when working with many objective problems.
Self-Organizing Maps with Variable Input Length for Motif Discovery and Word Segmentation
Time Series Motif Discovery (TSMD) is defined as searching for patterns that are previously unknown and appear with a given frequency in time series.
Incremental Semantic Mapping with Unsupervised On-line Learning
This paper introduces an incremental semantic mapping approach, with on-line unsupervised learning, based on Self-Organizing Maps (SOM) for robotic agents.
A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds
Also, it is important to develop methods that are easy to parameterize in a way that is robust to the different characteristics of the data at hand.
A Semi-Supervised Self-Organizing Map for Clustering and Classification
There has been an increasing interest in semi-supervised learning in the recent years because of the great number of datasets with a large number of unlabeled data but only a few labeled samples.
A Neural Network Architecture for Learning Word-Referent Associations in Multiple Contexts
This article proposes a biologically inspired neurocomputational architecture which learns associations between words and referents in different contexts, considering evidence collected from the literature of Psycholinguistics and Neurolinguistics.
