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Publication

Sustainable Transfer Learning for Adaptive Robot Skills

Khalil Abuibaid; Vinit Vikas Hegiste; Nigora Gafur; Achim Wagner; Martin Ruskowski
In: Kosta Jovanovic; Aleksandar Rodic; Mirko Rakovic (Hrsg.). Advances in Service and Industrial Robotics. International Conference on Robotics in Alpe-Adria-Danube Region (RAAD-2025), Cham, Pages 389-397, ISBN 978-3-032-02106-9, Springer Nature Switzerland, 2025.

Abstract

Learning robot skills from scratch is often time-consuming, while reusing data promotes sustainability, and improves time efficiency. This study investigates policy transfer across different robotic platforms, focusing on peg-in-hole task using reinforcement learning (RL). Policy training is carried out on two different robots. Their policies are training from scratch, transferred and evaluated for zero-shot and fine-tuning. Results indicate that zero-shot transfer leads to lower success rates and relatively longer task execution times while fine-tuning significantly improves performance with fewer training time-steps. These findings highlight that policy transfer with adaptation techniques improves training efficiency and generalization, reducing the need for extensive retraining and supporting sustainable robotic learning.