60 citations · 145 across the 19 of their papers we have counts for
5 papers · 1 filter
Self-Adapting Goals Allow Transfer of Predictive Models to New Tasks
Kai Olav Ellefsen, Jim Torresen
A long-standing challenge in Reinforcement Learning is enabling agents to learn a model of their environment which can be transferred to solve other problems in a world with the sa…
Evolved embodied phase coordination enables robust quadruped robot locomotion
Jørgen Nordmoen, Tønnes F. Nygaard, Kai Olav Ellefsen +1
Overcoming robotics challenges in the real world requires resilient control systems capable of handling a multitude of environments and unforeseen events. Evolutionary optimization…
Guiding Neuroevolution with Structural Objectives
Kai Olav Ellefsen, Joost Huizinga, Jim Torresen
The structure and performance of neural networks are intimately connected, and by use of evolutionary algorithms, neural network structures optimally adapted to a given task can be…
How do Mixture Density RNNs Predict the Future?
Kai Olav Ellefsen, Charles Patrick Martin, Jim Torresen
Gaining a better understanding of how and what machine learning systems learn is important to increase confidence in their decisions and catalyze further research. In this paper, w…
Multiobjective Coverage Path Planning: Enabling Automated Inspection of Complex, Real-World Structures
Kai Olav Ellefsen, Herman A. Lepikson, Jan C. Albiez
An important open problem in robotic planning is the autonomous generation of 3D inspection paths -- that is, planning the best path to move a robot along in order to inspect a tar…