When a robot is learning it needs to explore its environment and how its environment responds on its actions. When the environment is large and there are a large number of possible actions the robot can take, this exploration phase can take prohibitively long. However, exploration can often be optimised by letting a human expert guide the robot during its learning. Interactive machine learning,...

Reinforcement learning approach is learning how to map environment situations to actions, to maximize a reward signal. In this project, the SARSA(l) algorithm is applied to teach a robot to follow a wall and avoid running into obstacles. The robot is equipped with a laser scanner and odometry to perform the learning. The robot senses environment situation from the laser readings and decides whi...

I summarize research toward a robot learning architecture intended to enable a mobile robot to learn a wide range of find-and-fetch tasks. In particular, this paper summarizes recent research in the Learning Robots Laboratory at Carnegie Mellon University on aspects of robot learning, and our current work toward integrating and extending this within a single architecture. In previous work we de...

We applied Reinforcement Learning (RL) on a real robot arm actuated by two pneumatic artificial muscles that expose a highly nonlinear behaviour. To facilitate learning, we developed an empirical model based on real robot observations. Using the learned simulation model, reinforcement learning was able to quickly learn good robot controllers.

In many industrial robot applications it is a fact that the robot is programmed to do the same task repeatedly. By observing the control error in the different iterations of the same task it becomes clear that it is actually highly repetitive. Iterative Learning Control (ILC) allows to iteratively compensate for and, hence, remove this repetitive error. In the thesis different aspects of iterat...

Learning robots that can acquire new motor skills and refine existing ones have been a long-standing vision of both robotics, and machine learning. However, off-the-shelf machine learning appears not to be adequate for robot skill learning, as it neither scales to anthropomorphic robotics nor do fulfills the crucial real-time requirements. As an alternative, we propose to divide the generic ski...

In this paper an adaptive neuro fuzzy inference system based on interval Gaussian type-2 fuzzy sets in the antecedent part and Gaussian type-1 fuzzy sets as coefficients of linear combination of input variables in the consequent part is presented. The capability of the proposed method (we named ANFIS2) to function approximation and dynamical system identification is shown. The ANFIS2 structure ...

This paper concerns with the problem of deep learning parameters tuning using a genetic algorithm (GA) in order to improve the performance of deep learning (DL) method. We present a GA based DL method for robot object recognition and grasping. GA is used to optimize the DL parameters in learning procedure in term of the fitness function that is good enough. After finishing the evolution process...

We propose an integrated technique of genetic programming (GP) and reinforcement learning (RL) that allows a real robot to execute real-time learning. Our technique does not need a precise simulator because learning is done with a real robot. Moreover, our technique makes it possible to learn optimal actions in real robots. We show the result of an experiment with a real robot AIBO and represen...

The robot soccer system is being used as a test bed to develop the next generation of field robots. In the multiagent system, action selection is important for the cooperation and coordination among agents. There are many techniques in choosing a proper action of the agent. As the environment is dynamic, reinforcement learning is more suitable than supervised learning. Reinforcement learning is...