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Approximate Real-Time Optimal Control Based on Sparse Gaussian Process Models

Add to EJ Playlist  a.k.a. Learning to Swing-Up and Balance from Scratch in under 3 Minutes Revisiting the classical cart-pole balancing system (https://www.yo ?v=Lt-KLtkDlh8) , we present a fully automated system for (approximate) optimal control of non-linear systems. Our approach jointly learns a non-parametric model of the system dynamics and performs receding horizon control. This results in an extremely data-efficient learning algorithm that can operate under real-time constraints. Our algorithm successfully learns to control a real cart-pole system from-scratch (without prior knowledge provided by an expert), in less than 10 episodes of interaction with the system, amounting to less than 3 minutes of real time. For details, see: Joschka Boedecker, Jost Tobias Springenberg, Jan W├╝lfing, Martin Riedmiller (2014) Approximate Real-Time Optimal Control Based on Sparse Gaussian Process Models. In Adaptive Dynamic Programming and Reinforcement Learning (ADPRL).

Carrera Vision Tracking

Add to EJ Playlist  Summary of the bachelor thesis project by Manuel Watter. Using Neural Fitted Q-Iteration, a robot head first learns to keep a stationary object centered in the camera, and then to adjust its actions so as to better keep it in view once it is moving.


Add to EJ Playlist  reinforcement learning slotcar system

Neural Controller for Ms. Pac-Man

Add to EJ Playlist  Learning a controller for the Ms. Pac-Man arcade game by trial and failure. The learning algorithm is TD lambda using a neural network for value function representation and advanced features as input.

BCI for High-Level Remote Reaching and Grasping

Add to EJ Playlist  Showcase of the prototype system developed during the first stage of the NeuroBots project ( ainlinks-braint ools.uni-freibu projects/projec t-details/neuro bots) as a collaboration between the iEEG Lab (http://www.iee g.uni-freiburg. de) and the Machine Learning Lab ( rmatik.uni-frei of the University of Freiburg. Imagined motor commands are used for high-level remote control of an autonomous, reinforcement-l earning-based robotic system for reaching and grasping several kilometers away.

Combining EEG and Reinforcement Learning for Robot Control

Add to EJ Playlist  Teaser video for the BrainLinks-Brai nTools Cluster of Excellence ( ainlinks-braint ools.uni-freibu Improving on our previous system ( v=ZIQ6uyptAB8), we use electroencephal ography to communicate higher-level intentions to an autonomous controller learned via Neural Fitted Q-Iteration.

Combining EOG and NFQ for Robot Control

Add to EJ Playlist  First attempt at combining electrooculogra phy and machine learning to have a Katana robotic arm search for an object and pick it up. Collaboration between the Machine Learning Lab and the Biomedical Microsystems Group.

Passing Soccer Robots

Add to EJ Playlist  Pass play in robot soccer. Actually, this was the very first completed pass of the Brainstormers Tribots in an official tournament.

RoboCup 2007 Atlanta, Brainstormers Tribots Mix

Add to EJ Playlist  Best scenes from the RoboCup 2007 tournament in Atlanta.

Evolution of the Brainstormers Tribots

Add to EJ Playlist  This video summarizes the development process of the Tribots robot soccer team from the first experiments in a student project until winning the world championships 2006 and 2007.

Learning to Dribble by Success and Failure

Add to EJ Playlist  The behavior for dribbling the ball is trained by reinforcement learning

A Visual Servoing Approach to Manipulation using Neural Reinforcement Learning

Add to EJ Playlist  A Kinova Jaco robotic arm is expected to learn how to reach for an object and pick it up autonomously. Control is realized in a visual feedback control loop, making it both reactive and robust to noise. The controller is learned from scratch, without prior knowledge of proper behaviour, by success or failure using Neural Fitted Q Iteration.

Visual Deep Learning

Add to EJ Playlist  Swinging Up and Balancing a Real Pole

Harting Workshop

Add to EJ Playlist  Programming workshop with apprentices of Harting

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