Continual Learning for Embodied Agents: Methods, Evaluation and Practical Use

a Systematic Literature Review

Bachelor Thesis (2024)
Author(s)

A. Dascalu (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

C.A. Raman – Mentor (TU Delft - Pattern Recognition and Bioinformatics)

O.K. Shirekar – Mentor (TU Delft - Pattern Recognition and Bioinformatics)

W.P. Brinkman – Graduation committee member (TU Delft - Interactive Intelligence)

Faculty
Electrical Engineering, Mathematics and Computer Science
More Info
expand_more
Publication Year
2024
Language
English
Graduation Date
25-06-2024
Awarding Institution
Delft University of Technology
Project
CSE3000 Research Project
Programme
Computer Science and Engineering
Faculty
Electrical Engineering, Mathematics and Computer Science
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Continual learning (CL) enables intelligent systems to continually acquire, adapt, and apply knowledge, representing a dynamic paradigm in AI. For embodied agents—interacting with their environment physically and cognitively—CL enhances adaptability and reduces training costs significantly. In this literature review, we contribute by focusing on the application of CL in such agents, showcasing the approaches, means of evaluation and practical uses of this cognitive framework in real-world scenarios. We conclude that while CL holds promise for embodied agents, there exists a notable gap between the theoretical evaluation of CL and the complex real-world scenarios these agents operate in.

Files

License info not available