A Cloud based Reinforcement Learning Framework for humanoid grasping

This work presents an innovative approach to a common task on robotics: grasping a set of objects. Autonomously grasping a previously unknown object still remains a challenging problem. This Thesis presents a new framework, inspired by the classical sense-model-act architecture and the knowledge processing of Cognitive Robotics. The framework tries to generalize the grasping task to