60 citations · 145 across the 19 of their papers we have counts for
21 papers
CRRL: A Causality-Based Reinforcement Learning Framework for Autonomous System Recovery
Safia Fatima, Kai Olav Ellefsen, Leon Moonen
Traditional reinforcement learning (RL) for recovery in autonomous systems lacks causal understanding and generalizes poorly to novel failure scenarios. RL policies often stall in…
Memory-Efficient Policy Libraries with Low-Rank Adaptation in Reinforcement Learning
Samuel Valland Lyngset, Tor Viljen Raanaas, Gard Sveipe +4
When fine-tuning Large Language Models (LLMs), there has been success in minimizing both memory usage and computation with Parameter-Efficient Fine-Tuning (PEFT), like Low Rank Ada…
Lamarckian Inheritance in Dynamic Environments: How Key Variables Affect Evolutionary Dynamics
K. Ege de Bruin, Kyrre Glette, Kai Olav Ellefsen
The co-optimization of a robot's body and brain presents a coupled challenge: the morphology constrains which control strategies are effective, while the control determines how wel…
Social Learning Strategies for Evolved Virtual Soft Robots
K. Ege de Bruin, Kyrre Glette, Kai Olav Ellefsen +2
Optimizing the body and brain of a robot is a coupled challenge: the morphology determines what control strategies are effective, while the control parameters influence how well th…
Integrating Sample Inheritance into Bayesian Optimization for Evolutionary Robotics
K. Ege de Bruin, Kyrre Glette, Kai Olav Ellefsen
In evolutionary robotics, robot morphologies are designed automatically using evolutionary algorithms. This creates a body-brain optimization problem, where both morphology and con…
Generational Replacement and Learning for High-Performing and Diverse Populations in Evolvable Robots
K. Ege de Bruin, Kyrre Glette, Kai Olav Ellefsen
Evolutionary Robotics offers the possibility to design robots to solve a specific task automatically by optimizing their morphology and control together. However, this co-optimizat…
More complex environments may be required to discover benefits of lifetime learning in evolving robots
Ege de Bruin, Kyrre Glette, Kai Olav Ellefsen
It is well known that intra-life learning, defined as an additional controller optimization loop, is beneficial for evolving robot morphologies for locomotion. In this work, we inv…
Offline Skill Generalization via Task and Motion Planning
Shin Watanabe, Geir Horn, Jim Tørresen +1
This paper presents a novel approach to generalizing robot manipulation skills by combining a sampling-based task-and-motion planner with an offline reinforcement learning algorith…
Evolution of linkages for prototyping of linkage based robots
Emma Stensby Norstein, Kai Olav Ellefsen, Frank Veenstra +2
Prototyping robotic systems is a time consuming process. Computer aided design, however, might speed up the process significantly. Quality-diversity evolutionary approaches optimis…
Open-ended search for environments and adapted agents using MAP-Elites
Emma Stensby Norstein, Kai Olav Ellefsen, Kyrre Glette
Creatures in the real world constantly encounter new and diverse challenges they have never seen before. They will often need to adapt to some of these tasks and solve them in orde…
Co-optimising Robot Morphology and Controller in a Simulated Open-Ended Environment
Emma Hjellbrekke Stensby, Kai Olav Ellefsen, Kyrre Glette
Designing robots by hand can be costly and time consuming, especially if the robots have to be created with novel materials, or be robust to internal or external changes. In order…
A Model of WiFi Performance With Bounded Latency
Bjørn Ivar Teigen, Neil Davies, Kai Olav Ellefsen +2
In September 2020, the Broadband Forum published a new industry standard for measuring network quality. The standard centers on the notion of quality attenuation. Quality attenuati…
MAP-Elites enables Powerful Stepping Stones and Diversity for Modular Robotics
Jørgen Nordmoen, Frank Veenstra, Kai Olav Ellefsen +1
In modular robotics, modules can be reconfigured to change the morphology of the robot, making it able to adapt for specific tasks. However, optimizing both the body and control is…
Quality and Diversity in Evolutionary Modular Robotics
Jørgen Nordmoen, Frank Veenstra, Kai Olav Ellefsen +1
In Evolutionary Robotics a population of solutions is evolved to optimize robots that solve a given task. However, in traditional Evolutionary Algorithms, the population of solutio…
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…
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
Joel Lehman, Jeff Clune, Dusan Misevic +50
Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic pr…
Deep Predictive Models in Interactive Music
Charles P. Martin, Kai Olav Ellefsen, Jim Torresen
Musical performance requires prediction to operate instruments, to perform in groups and to improvise. In this paper, we investigate how a number of digital musical instruments (DM…