Post on 23-Dec-2015
CoTeSys-ROS Fall SchoolMunich November 3th 2010
General Concept & First Demonstrator
Jos ElfringRob Janssen
General concept• Humans in day2day tasks
• 90% feedforward – learning and memorization• 10% feedback – sensory feedback
j.elfring@tue.nlr.j.m.janssen@tue.nl
General concept• Humans in day2day tasks
• 90% feedforward – learning and memorization• 10% feedback – sensory feedback
• RoboEarth – learning and memorization• Share any reusable knowledge between robots• World-Wide-Web for and by robots
j.elfring@tue.nlr.j.m.janssen@tue.nl
General concept• Humans in day2day tasks
• 90% feedforward – learning and memorization• 10% feedback – sensory feedback
• RoboEarth – learning and memorization• Share any reusable knowledge between robots• World-Wide-Web for and by robots
• Four year EC funding (FP7)
j.elfring@tue.nlr.j.m.janssen@tue.nl
Concept
j.elfring@tue.nlr.j.m.janssen@tue.nl
Concept
j.elfring@tue.nlr.j.m.janssen@tue.nl
Concept
j.elfring@tue.nlr.j.m.janssen@tue.nl
Concept
j.elfring@tue.nlr.j.m.janssen@tue.nl
Example
j.elfring@tue.nlr.j.m.janssen@tue.nl
Example
j.elfring@tue.nlr.j.m.janssen@tue.nl
Bed
Example
j.elfring@tue.nlr.j.m.janssen@tue.nl
Bed
Cabinet
Example
j.elfring@tue.nlr.j.m.janssen@tue.nl
Bed
Cabinet
Power plug
Example
j.elfring@tue.nlr.j.m.janssen@tue.nl
Bed
Cabinet
Power plug
Bed
Cabinet
Power plug
Example
j.elfring@tue.nlr.j.m.janssen@tue.nl
Bed
Cabinet
Power plug
Bed
Cabinet
Power plug
Example
j.elfring@tue.nlr.j.m.janssen@tue.nl
Bed
Cabinet
Power plug
Bed
Cabinet
Power plug
Bed
Cabinet
Power plug
‘Birth of RoboEarth workshop’Two classes of robots sharing knowledge through RoboEarth
TUE Eindhoven bot RFC Stuttgart bot
j.elfring@tue.nlr.j.m.janssen@tue.nl
Experiment description
• The two classes will collaboratively explore a maze• Sharing of knowledge on two levels
• Sharing of learned inputs for moving 1[m] front, back, left and right within class
• Sharing of environmental knowledge on the maze between classes
j.elfring@tue.nlr.j.m.janssen@tue.nl
Visualisation
1. Learning input primitives (using Iterative learning Control)
j.elfring@tue.nlr.j.m.janssen@tue.nl
1 [m]
Visualisation
1. Learning input primitives (using Iterative learning Control)
j.elfring@tue.nlr.j.m.janssen@tue.nl
1 [m]
1 [m]
Visualisation
1. Learning input primitives (using Iterative learning Control)
j.elfring@tue.nlr.j.m.janssen@tue.nl
1 [m]
1 [m]
1 [m]
Visualisation
1. Learning input primitives (using Iterative learning Control)
j.elfring@tue.nlr.j.m.janssen@tue.nl
1 [m]
1 [m]
1 [m]
1 [m]
Visualisation
1. Learning input primitives (using Iterative learning Control)
j.elfring@tue.nlr.j.m.janssen@tue.nl
1 [m]
1 [m]
1 [m]
1 [m]
Visualisation
1. Learning input primitives (using Iterative learning Control)
j.elfring@tue.nlr.j.m.janssen@tue.nl
1 [m]
1 [m]
1 [m]
1 [m]
Visualisation
2. Use primitives to collaboratively explore a maze (using Q-learning)
j.elfring@tue.nlr.j.m.janssen@tue.nl
Summary
All though imposed methods in this experiment rather trivial, it can been shown that
• robots benefit from sharing knowledge (increased efficiency)• scalable• many forms of knowledge can be shared (object information,
environmental data, action recipes)• RoboEarth databases function as backup servers• sharing through WWW can be used globally
j.elfring@tue.nlr.j.m.janssen@tue.nl
j.elfring@tue.nlr.j.m.janssen@tue.nl