Viewpoint optimization for aiding grasp synthesis algorithms using reinforcement learning

Grasp synthesis for unknown objects is a challenging problem as the algorithms are expected to cope with missing object shape information. This missing information is a function of the vision sensor viewpoint. The majority of the grasp synthesis algorithms in literature synthesize a grasp by using one single image of the target object and...

Active vision via extremum seeking for robots in unstructured environments: Applications in object recognition and manipulation

In this paper, a novel active vision strategy is proposed for optimizing the viewpoint of a robot's vision sensor for a given success criterion. The strategy is based on extremum seeking control (ESC), which introduces two main advantages: 1) Our approach is model free: It does not require an explicit objective function or any other task...

Evaluation of physical damage associated with action selection strategies in reinforcement learning

Reinforcement learning techniques enable robots to deal with their own dynamics and with unknown environments without using explicit models or preprogrammed behaviors. However, reinforcement learning relies on intrinsically risky exploration, which is often damaging for physical systems. In the case of the bipedal walking robot Leo, which is...