Reinforcement learning offers to robotics a framework and set of tools for the design of
sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic …

It is known that most of the key problems in visual servo control of robots are related to the
performance analysis of the system considering measurement and modeling errors. In this …

The paper describes our first experiments on reinforcement learning to steer a real robot car.
The applied method, neural fitted Q iteration (NFQ) is purely data-driven based on data …

In this paper, path planning and sorting of packages for Omnidirectional-Wheel conveyor are
presented using Reinforcement Learning (RL). Q-learning, Double Q-learning, Deep Q …

In this thesis, we discuss approaches that allow robots to learn motor skills. Motor skills can
often be represented by motor primitives, which encode elemental motions. To date, there …

Visual-guided locomotion for snake-like robots is a challenging task, since it involves not
only the complex body undulation with many joints, but also a joint pipeline that connects the …

Present space robots such as planetary robots and flexible robots have structural flexibility in
their arms and joints that leads to an error in the tip positioning owing to tip deflection. The …

Due to the lack of enough generalization in the state space, common methods of
reinforcement learning suffer from slow learning speed, especially in the early learning trials …

The paper discusses the steps necessary to set up a neural reinforcement controller for
successfully solving typical (real world) control tasks. The major intention is to provide a …

Behavior based architecture gives robot fast and reliable action. If there are many behaviors
in robot, behavior coordination is needed. Subsumption architecture is behavior …