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Transfer learning and limited data learning techniques applied to autonomous robotics

Year: 2025

Type: article

Abstract: This study reviews recent developments in the field of transferable learning, with direct applicability to autonomous robotics. Through a comparative approach, the paper reviews the conceptual and methodological foundations, highlighting how few-shot and zero-shot learning techniques support the adaptability of robots to novel tasks under data-limited conditions. Relevant applications ‒ ranging from navigation and manipulation to human-robot interaction ‒ are discussed, illustrating the capabilities of these methods. The study also identifies a number of major constraints that prevent the widespread adoption of these techniques and provides recommendations for future research oriented towards transfer scenarios from a simulated environment to reality and between robotic platforms with different kinematics.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.18711329

Authors: Mangeac, Constantin-Rosmin, Constantin, George

Institution: Universitatea Națională de Știință și Tehnologie Politehnica București

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7130702326

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