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arXiv · 2604.06943

Sustainable Transfer Learning for Adaptive Robot Skills

Khalil Abuibaid·Vinit Hegiste·Nigora Gafur·Achim Wagner·Martin Ruskowski
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Abstract

Learning robot skills from scratch is often time-consuming, while reusing data promotes sustainability and improves sample 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 transferred and evaluated for zero-shot, fine-tuning, and training from scratch. 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 sample efficiency and generalization, reducing the need for extensive retraining and supporting sustainable robotic learning.

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Khalil Abuibaid, Vinit Hegiste, Nigora Gafur, Achim Wagner, Martin Ruskowski. 2026-04-08. Sustainable Transfer Learning for Adaptive Robot Skills. https://doi.org/10.1007/978-3-032-02106-9_43

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