Related papers: Sustainable Transfer Learning for Adaptive Robot Skills

Sustainable Transfer Learning for Adaptive Robot Skills

URL: http://arxiv.org/abs/2604.06943v1
Date: Wed, 08 Apr 2026 11:06:10 GMT
Title: Sustainable Transfer Learning for Adaptive Robot Skills
Abstract summary: This study investigates policy transfer across different robotic platforms, focusing on peg-in-hole task using reinforcement learning (RL)<n>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.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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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