Mechanisms for Online Adaptation in robots: a comparative study
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Abstract
Robots deployed in dynamic environments must be able to adapt autonomously to changing conditions and perturbations. This thesis examines online adaptation strategies for minimally cognitive robotic agents, with a focus on their ability to achieve and sustain high performance. We explore a range of adaptive controllers, including architectures inspired by Braitenberg vehicles and Artificial Neural Network-based strategies, from simple feed-forward topologies to recurrent networks with internal memory, each tested in navigation with a collision avoidance task. Our experimental results compare the performance of various mechanisms and adaptation policies, highlighting the trade-offs between reactivity, memory, and robustness in different online adaptation settings.
@mastersthesis{pacilli2025mechanisms,
title = {Mechanisms for online adaptation in robots: A comparative study},
author = {Pacilli, Benedetta},
year = {2025},
school = {University of Bologna},
note = {Supervisor: Roli, Andrea, Co-Supervisor: Baldini, Paolo}
}