Machine Learning and Robotics

Robotski operativni sustav je skup programskih okvira koji omogućuju razvoj programske podrške za robotske sustave. Sastoji se od skupa nezavisnih komponenata razvijenih od strane inženjera i istraživača. Implementiran je kao distribuirani sustav s mehanizmom objave/pretplate, u kojem je svaki program čvor, što se prikazuje ROS grafom. Nadalje, moguće ga je integrirati s naprednim tehnologijama s područja strojnog učenja što otvara vrata novim istraživanjima i spoznajama. Potporno učenje je grana strojnog učenja koja se formalizira koristeći model Markovljevog procesa odlučivanja. Fundamentalne karakteristike potpornog učenja su interaktivno učenje kroz pokušaj i pogrešku (učenje kroz iskustvo) te cilj u obliku maksimizacije kumulativne sume nagrada uz odabrani faktor propadanja. Velik izazov u potpornom učenju je i problem ravnoteže između istraživanja i eksploatacije.Robotic operating system is a collection of software frameworks for robot software development. ROS is composed from a set of independent components that are developed and maintained by various engineers and researchers. It is implemented as a distributed system along with the publish/subscribe mechanism, where every program is a node, which is visualized by the ROS graph. Furthermore, integration of ROS with advanced machine learning technologies is also possible, which brings new opportunities for research and cognition. Reinforcement learning is a machine learning sub-area that is formalized using the Markov decision process model. Fundamental reinforcement learning characteristics include interactive learning by trial and error (learning by experience) and a goal in the shape of maximizing the discounted cumulative sum of rewards. Also, one of the great challenges of reinforcement learning is the balance between exploration and exploitation