Fields of competence
ROBOTNOR represents a unique synergy of academic and industrial expertise which allows us to counsel and comprehend a wide variety of tasks and projects. Our fields of competence are numerous and diverse.
Bestowing our machines with intelligence has been an ambitious goal for scientists since the birth of artificial intelligence as a field in the 1950’s. And as we scientists learn to pace ourselves, the field of artificial intelligence has in the last two decades matured into actually usable techniques for robots. ROBOTNOR conducts research on how robots can automatically improve based on experience. We refer to this as robot learning.
Our research on robots that learn is founded on a desire to create more usable robots for end-users. By making a robot able to learn, we effectively reduce the need for programming the robot. The idea is to enable the robot to learn how to perform its intended task, either by being shown how to perform the task or by being told what its goal is (what to do) instead of how to do it. Subsequently, we would like the robot to experiment (or play) with its environment in order to discover solutions on its own. You may think of this approach as a new kind of user interface for robots.
The robot learning laboratory is not confined to any specific robot system, but acts as […]
Often one sensor or one measurement principle can not capture all the information needed to […]
Machine vision is an essential part of most robotic system and is generally described as […]
Collision detection and avoidance are crucial components in any moving system that needs to interact […]
Vision sensors are necessary for the robots to understand and interact with their surroundings. Depending […]
Launched in 2009, this project aims to develop the next generation robotic technology for Norwegian […]
Mobile robot manipulators (mobile robots with one or more attached manipulator arms) will be prevalent […]
This project considers cost-effective monitoring for remote environmental friendly inspection and maintenance of offshore wind turbines.
S. M. Albrektsen, S. A. Fjerdingen: “Safe Robot Learning by Energy Limitation”, in Proc. 5th Int. Conf. Intelligent Robotics and Applications (ICIRA 2012).
S. A. Fjerdingen, M. Bjerkeng, A. A. Transeth, E. Kyrkjebø, A. Røyrøy: “A Learning Camera Platform for Remote Operations with Industrial Manipulators”, in IFAC Workshop on Automatic Control in Offshore Oil and Gas Production, 2012.
S. A. Fjerdingen, E. Kyrkjebø: “Safe Reinforcement Learning for Continuous Spaces through Lyapunov-Constrained Behavior”, in Frontiers in Artificial Intelligence and Applications, 2011.
F. J. Marin, J. Casillas, M. Mucientes, A. A. Transeth, S. A. Fjerdingen, I. Schjølberg: “Learning Intelligent Controllers for Path-Following Skills on Snake-Like Robots”, in Lecture Notes in Computer Science, 2011.
S. A. Fjerdingen, E. Kyrkjebø, A. A. Transeth: “AUV Pipeline Following using Reinforcement Learning”, in Proceedings for the Joint Conference of ISR/ROBOTIK, 2010.