5 papers
Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer Architecture
Daniel Tanneberg, Elmar Rueckert, Jan Peters
A key feature of intelligent behavior is the ability to learn abstract strategies that transfer to unfamiliar problems. Therefore, we present a novel architecture, based on memory-…
Cataglyphis ant navigation strategies solve the global localization problem in robots with binary sensors
Nils Rottmann, Ralf Bruder, Achim Schweikard +1
Low cost robots, such as vacuum cleaners or lawn mowers, employ simplistic and often random navigation policies. Although a large number of sophisticated localization and planning…
Loop Closure Detection in Closed Environments
Nils Rottmann, Ralf Bruder, Achim Schweikard +1
Low cost robots, such as vacuum cleaners or lawn mowers employ simplistic and often random navigation policies. Although a large number of sophisticated mapping and planning approa…
Experience Reuse with Probabilistic Movement Primitives
Svenja Stark, Jan Peters, Elmar Rueckert
Acquiring new robot motor skills is cumbersome, as learning a skill from scratch and without prior knowledge requires the exploration of a large space of motor configurations. Acco…
Learning walk and trot from the same objective using different types of exploration
Zinan Liu, Kai Ploeger, Svenja Stark +2
In quadruped gait learning, policy search methods that scale high dimensional continuous action spaces are commonly used. In most approaches, it is necessary to introduce prior kno…