In this paper we describe an adaptive mobile robot control system that enables a multi-sensor robot to learn reactive behaviours by interacting with the environment. The controller is particularly suitable for exploratory robots due to its ability to adapt to unknown environments and recover from partial sensor damage. Learning is based on the robot learning a map between sensors and trajectory...

Robot learning by imitation requires the detection of a tutor's action demonstration and its relevant parts. Current approaches implicitly assume a unidirectional transfer of knowledge from tutor to learner. The presented work challenges this predominant assumption based on an extensive user study with an autonomously interacting robot. We show that by providing feedback, a robot learner influe...

Robot execution failures prediction (classification) in the robot tasks is a difficult learning problem due to partially corrupted or incomplete measurements of data and unsuitable prediction techniques for this prediction problem with little learning samples. Therefore, how to predict the robot execution failures problem with little (incomplete) or erroneous data deserves more attention in the...

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...

Behavior based architecture is widely used in mobile robot because it makes the robot response faster. If robot only works to achieve simple task, it can use some primitive behaviors. However, when the task is getting more complex, the behavior coordination is needed. In order to construct this coordinator, fuzzy logic can be applied as Fuzzy Behavior Coordinator (FBC). By using FBC, it can be ...

In this paper we present a new method for robot real time policy adaptation by combining learning and evolution. The robot adapts the policy as the environment conditions change. In our method, we apply evolutionary computation to find the optimal relation between reinforcement learning parameters and robot performance. The proposed algorithm is evaluated in the simulated environment of the Cyb...

In an exploratory study on educational child-robot interaction we investigate the effect of alternating a learning activity with an additional shared activity. Our aim is to enhance and enrich the relationship between child and robot by introducing “physical extracurricular activities”. This enriched relationship might ultimately influence the way the child and robot interact with the learning ...

We approach floor-level robotic manipulation from a Learning from Demonstration direction. In this paradigm, a robot learns new tasks in new environments from observations of the task itself. Many current robot learning algorithms require the existence of basic behaviors that can be combined to perform the desired task. However, robots that exist in the world for long timeframes may exhaust thi...

In a robotic soccer team, goalkeeper is an important challenging role, which has different characteristics from the other teammates. This paper proposes a new learning-based behavior model for a soccer goalkeeper robot by using Petri nets. The model focuses on modeling and analyzing, both qualitatively and quantitatively, for the goalkeeper role so that we have a model-based knowledge of the ta...