跨机器人迁移策略，提升技能学习效率与可持续性。
Sustainable Transfer Learning for Adaptive Robot Skills
- 在不同机器人上训练策略并迁移，评估零样本、微调与从头训练效果。
- 微调后成功率显著提升，所需训练步数远少于从头训练。
- 适合需要快速适应新机器人的智能系统研发人员。
从零学习机器人技能通常耗时较长，而重用数据可提升可持续性并改善样本效率。本研究聚焦于在不同机器人平台间迁移策略，采用强化学习（RL）实现插销入孔任务。在两种不同机器人上分别训练策略，并评估其在零样本、微调和从头训练下的表现。结果表明，零样本迁移的成功率较低且任务执行时间更长；而微调显著提升性能，所需训练步数大幅减少。这些发现说明，结合适应技术的策略迁移能有效提高样本效率与泛化能力，减少重复训练需求，支持可持续的机器人学习。
原文摘要 · Abstract (English)
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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