Search Results for author:
Found 9 papers, 4 papers with code
Learning to Play Soccer From Scratch: Sample-Efficient Emergent Coordination through Curriculum-Learning and Competition
In addition, we propose using experience sharing, a method that shares experience from a fixed opponent, trained in a previous stage, for training the agent currently learning, and a form of frame-skipping, to raise performance significantly.
Continuous Control for High-Dimensional State Spaces: An Interactive Learning Approach
However, DRL has several limitations when used in real-world problems (e. g., robotics applications).
Playing Soccer without Colors in the SPL: A Convolutional Neural Network Approach
The goal of this paper is to propose a vision system for humanoid robotic soccer that does not use any color information.
Visual SLAM-based Localization and Navigation for Service Robots: The Pepper Case
We propose a Visual-SLAM based localization and navigation system for service robots.
Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks
Deep Reinforcement Learning (DRL) has become a powerful strategy to solve complex decision making problems based on Deep Neural Networks (DNNs).
A Survey on Deep Learning Methods for Robot Vision
Given that deep learning has already attracted the attention of the robot vision community, the main purpose of this survey is to address the use of deep learning in robot vision.
Recognition of Grasp Points for Clothes Manipulation under unconstrained Conditions
Then, the proposed system applies the Vessel Enhancement filter to identify wrinkles in the clothes, allowing to compute a roughness index for the clothes.
Toward Real-Time Decentralized Reinforcement Learning using Finite Support Basis Functions
This paper addresses the design and implementation of complex Reinforcement Learning (RL) behaviors where multi-dimensional action spaces are involved, as well as the need to execute the behaviors in real-time using robotic platforms with limited computational resources and training times.
Using Convolutional Neural Networks in Robots with Limited Computational Resources: Detecting NAO Robots while Playing Soccer
The main goal of this paper is to analyze the general problem of using Convolutional Neural Networks (CNNs) in robots with limited computational capabilities, and to propose general design guidelines for their use.
