Learning provides a useful tool for the automatic design of autonomous robots. Recent research on learning robot control has predominantly focussed on learning single tasks that were studied in isolation. If robots encounter a multitude of control learning tasks over their entire lifetime, however, there is an opportunity to transfer knowledge between them. In order to do so, robots may learn t...

Reinforcement learning (RL) is a general and well-known method that a robot can use to learn an optimal control policy to solve a particular task. We would like to build a versatile robot that can learn multiple tasks, but using RL for each of them would be prohibitively expensive in terms of both time and wear-and-tear on the robot. To remedy this problem, we use the Policy Gradient Efficient ...

Communication is not just the manipulation of words, but needs to decide what is communicated considering the surrounding situations and to understand the communicated signals considering how to reflect it on the actions. In this paper, aiming to the emergence of purposive and grounded communication, communication is seamlessly involved in the entire process consisted of one neural network, and...

This paper describes a neural network model for the reactive behavioral navigation of a mobile robot. From the information received through the sensors the robot can elicit one of several behaviors (e.g. stop, avoid, stroll, wall following), through a competitive neural network. The robot is able to develop a control strategy depending on sensor information and learning operation. Reinforcement...

In the quest for machines that are able to learn, reinforcement learning (RL) is found to be an appealing learning methodology. A known problem in this learning method, however, is that it takes too long before the robot learns to associate suitable situation-action pairs. Due to this problem, RL has remained applicable only to simple tasks and discrete environment. To accelerate the learning p...

The development of robots that learn from experience is a relentless challenge confronting artificial intelligence today. This paper describes a robot learning method which enables a mobile robot to simultaneously acquire the ability to avoid objects, follow walls, seek goals and control its velocity as a result of interacting with the environment without human assistance. The robot acquires th...

One of the standing challenging aspects in mobile robotics is the ability to navigate autonomously. It is a difficult task, which requiring a complete modeling of the environment. This paper presents an intelligent navigation method for an autonomous mobile robot which requires only a scalar signal like a feedback indicating the quality of the applied action. Instead of programming a robot, we ...

This paper outlines some ideas as to how robot learning experiments might best be designed. There are three principal ndings: (i) in order to evaluate robot learners we must employ multiple evaluation methods together; (ii) in order to measure in any absolute way the performance of a learning algorithm we must characterise the complexity of the underlying decision task formed by the interaction...

Interacting with unknown objects, and learning and producing e↵ective grasping procedures in particular, are challenging problems for robots. This paper proposes an intrinsically motivated reinforcement learning mechanism for learning to grasp uknown objects. The mechanism uses frustration to determine when grasping of an object is not possible. The critical threshold of frustration is dynamica...

Behaviors coordination is one of keypoints in behavior based robotics. Subsumption architecture and motor schema are example of their methods. In order to study their characteristics, experiments in physical robot are needed to be done. It can be concluded from experiment result that the first method gives quick, robust but non smooth response. Meanwhile the latter gives slower but smoother res...