Learning Where to Park
Jan 1, 2020·,,
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0 min read
Burak ergul
Thijs van de laar
Magnus t% c3% b8nder koudahl
Martin Roa Villescas
Bert de vries
Abstract
We consider active inference as a novel approach to the design of synthetic autonomous agents. In order to assess active inference’s feasibility for real-world applications, we developed an agent that controls a ground-based robot. The agent contains a generative dynamic model for the robot’s position and for performance appraisals by an observer of the robot. Our experiments show that the agent is capable of learning the target parking position from the observer’s feedback and robustly steer the robot toward the learned target position.
Publication
In Poster session presented at 1st International Workshop on Active Inference
Authors
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Authors
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Teacher & Researcher
Martin Roa Villescas holds a BSc in Electronic Engineering from the National
University of Colombia and an MSc in Embedded Systems from Eindhoven
University of Technology (TU/e). He worked at Philips Research as an
embedded software designer from 2013 to 2018. He later returned to TU/e for
his doctoral research in model-based machine learning, carried out within
the PhD-Teaching Assistant trajectory combining research and teaching. Since
2023, he has been working at Fontys University of Applied Sciences in the
Netherlands, where he teaches in the Information and Communication
Technology program and conducts research in robotics and smart industry.
Authors