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Researcher profile

Kai Olav Ellefsen

Kai Olav Ellefsen contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
RoboticsArtificial IntelligenceNetworking and Internet ArchitectureNeural and Evolutionary ComputingPerformance

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Published work

5 published item(s)

preprint2026arXiv

Generational Replacement and Learning for High-Performing and Diverse Populations in Evolvable Robots

Evolutionary Robotics offers the possibility to design robots to solve a specific task automatically by optimizing their morphology and control together. However, this co-optimization of body and control is challenging, because controllers need some time to adapt to the evolving morphology - which may make it difficult for new and promising designs to enter the evolving population. A solution to this is to add intra-life learning, defined as an additional controller optimization loop, to each individual in the evolving population. A related problem is the lack of diversity often seen in evolving populations as evolution narrows the search down to a few promising designs too quickly. This problem can be mitigated by implementing full generational replacement, where offspring robots replace the whole population. This solution for increasing diversity usually comes at the cost of lower performance compared to using elitism. In this work, we show that combining such generational replacement with intra-life learning can increase diversity while retaining performance. We also highlight the importance of performance metrics when studying learning in morphologically evolving robots, showing that evaluating according to function evaluations versus according to generations of evolution can give different conclusions.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Integrating Sample Inheritance into Bayesian Optimization for Evolutionary Robotics

In evolutionary robotics, robot morphologies are designed automatically using evolutionary algorithms. This creates a body-brain optimization problem, where both morphology and control must be optimized together. A common approach is to include controller optimization for each morphology, but starting from scratch for every new body may require a high controller learning budget. We address this by using Bayesian optimization for controller optimization, exploiting its sample efficiency and strong exploration capabilities, and using sample inheritance as a form of Lamarckian inheritance. Under a deliberately low controller learning budget for each morphology, we investigate two types of sample inheritance: (1) transferring all the parent's samples to the offspring to be used as prior without evaluating them, and (2) reevaluating the parent's best samples on the offspring. Both are compared to a baseline without inheritance. Our results show that reevaluation performs best, with prior-based inheritance also outperforming no inheritance. Analysis reveals that while the learning budget is too low for a single morphology, generational inheritance compensates for this by accumulating learned adaptations across generations. Furthermore, inheritance mainly benefits offspring morphologies that are similar to their parents. Finally, we demonstrate the critical role of the environment, with more challenging environments resulting in more stable walking gaits. Our findings highlight that inheritance mechanisms can boost performance in evolutionary robotics without needing large learning budgets, offering an efficient path toward more capable robot design.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Lamarckian Inheritance in Dynamic Environments: How Key Variables Affect Evolutionary Dynamics

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 well the morphology performs. To address this, we combine morphology optimization as evolution with controller optimization as lifetime learning, utilizing Lamarckian inheritance to transfer learned controller parameters from parent to offspring. In dynamic environments, existing literature presents conflicting evidence: while traditional evolutionary theory often suggests Lamarckian inheritance lacks benefit, recent studies in evolutionary robotics indicate it can improve performance. We hypothesize that this is because previous works have not included all relevant variables with dynamic environments. In this work, we show that the benefit of Lamarckian inheritance depends on two variables: how conflicting the environmental changes are to robot control, and the predictability of those changes for the robotic agent. Using virtual soft robots and two different learning approaches, Bayesian optimization and reinforcement learning, we show that Lamarckian inheritance only underperforms Darwinian inheritance when the changes are both conflicting and unpredictable. We find that adding a sensor to detect environmental changes restores the benefits for Lamarckian inheritance in conflicting environments, by allowing robotic agents to predict the need for a different behavior, thereby generalizing their control.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

A Model of WiFi Performance With Bounded Latency

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 attenuation is a measure of the distribution of latency and packet loss between two points connected by a network path. A vital feature of the quality attenuation idea is that we can express detailed application requirements and network performance measurements in the same mathematical framework. Performance requirements and measurements are both modeled as latency distributions. To the best of our knowledge, existing models of the 802.11 WiFi protocol do not permit the calculation of complete latency distributions without assuming steady-state operation. We present a novel model of the WiFi protocol. Instead of computing throughput numbers from a steady-state analysis of a Markov chain, we explicitly model latency and packet loss. Explicitly modeling latency and loss allows for both transient and steady-state analysis of latency distributions, and we can derive throughput numbers from the latency results. Our model is, therefore, more general than the standard Markov chain methods. We reproduce several known results with this method. Using transient analysis, we derive bounds on WiFi throughput under the requirement that latency and packet loss must be bounded.

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preprint2020arXiv

Quality and Diversity in Evolutionary Modular Robotics

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 solutions tends to converge to local optima when the problem is complex or the search space is large, a problem known as premature convergence. Quality Diversity algorithms try to overcome premature convergence by introducing additional measures that reward solutions for being different while not necessarily performing better. In this paper we compare a single objective Evolutionary Algorithm with two diversity promoting search algorithms; a Multi-Objective Evolutionary Algorithm and MAP-Elites a Quality Diversity algorithm, for the difficult problem of evolving control and morphology in modular robotics. We compare their ability to produce high performing solutions, in addition to analyze the evolved morphological diversity. The results show that all three search algorithms are capable of evolving high performing individuals. However, the Quality Diversity algorithm is better adept at filling all niches with high-performing solutions. This confirms that Quality Diversity algorithms are well suited for evolving modular robots and can be an important means of generating repertoires of high performing solutions that can be exploited both at design- and runtime.

0 citations0 reviewsNo code linkedOpen access