Learning from demonstration algorithms enable a robot to learn a new policy based on demonstrations provided by a teacher. In this article, we explore a novel research direction, multi-robot learning from demonstration, which extends demonstration based learning methods to collaborative multi-robot domains. Specifically, we study the problem of enabling a single person to teach individual polic...

Reinforcement learning is a useful method to acquire a purposive behavior with little or no a priori knowledge about the environment. In applying the reinforcement learning to a real robot, there are many di cult problems, such as long learning time and construction of its state and action space. In this paper, we show a soccer robot which learns to shoot a ball into the goal using the Q-learni...

Motor schemas used for robot learning are sequences of action that accomplish a goal-directed behavior, or a task. Motor schemas in robot learning are also known as movement primitives, basis behaviors, units of action, and macro actions. Rather than representing the simplest elementary actions available to the robot, such as a simple command to a robot actuator, schemas and motion primitives r...

An intelligent control method is proposed for control of rigid robot manipulators which achieves exponential tracking of repetitive robot trajectory under uncertain operating conditions such as parameter uncertainty and unknown deterministic disturbance. In the learning controller, exponentially stable learning algorithms are combined with stabilizing computed error feedforward and feedback inp...

For the human-robot communicative interaction, it is difficult to design robots behavior which is preferable to human. In this paper, therefore, we propose an approach to personality characterization of the face robot generated by reinforcement learning with human instruction, and investigate its characteristics from some real human-robot interaction. We found that the face robot personality ch...

How can artificial neural nets generalize better from fewer examples? In order to generalize successfully, neural network learning methods typically require large training data sets. We introduce a neural network learning method that generalizes rationally from many fewer data points, relying instead on prior knowledge encoded in previously learned neural networks. For example, in robot control...

Learning in robotics has received more and more attention in recent years. It eases bridging the gap between low-level sensor data and high-level concepts. A high-level representation language is necessary in order to support the communication between robot and user in both directions when the robot navigates in unknown environments. Controlling robots in terms of a high-level representation fo...

Learning is essential to expand the capabilities of a robot. But what is the meaning of learning for a robot and how is the implementation of a learning task? This question is a key question and in addition to what should be learned. This paper defines a general learning model, classify robot learning and develops a learning architecture for locomotion of a simple walker. These learning archite...