发表机构
University of Alicante（阿利坎特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种结合领域随机化与特征级自适应的双变分自编码器混合迁移框架，以45225张模拟和4556张真实图像训练，实现低成本机器人室内导航近91%的分类成功率，并验证了在嵌入式平台上的效率。
AI 中文摘要
基于视觉的低成本机器人自主导航仍是一项基本挑战，主要源于模拟训练环境与真实世界运行条件之间的显著差距。直接从仿真进行策略迁移往往效果不佳，而仅依靠真实数据训练又不切实际。我们提出了一种混合迁移学习框架，通过结合领域随机化与特征级领域自适应，有效弥合了仿真到现实的差距。我们的方法采用具有共享解码器的双卷积变分自编码器架构，在45225张模拟图像和仅4556个真实世界样本的广泛数据集上进行训练。该架构学习了一个紧凑的、共同的潜在表示空间，使两个领域的分布对齐。自适应过程进一步通过两种互补的数据增强技术得到增强，旨在扩充有限的真实世界数据。实验评估表明，我们的方法在真实世界室内导航的图像分类任务中实现了近91%的平均成功率，显著优于仅仿真训练和仅真实世界训练。我们通过直接的真实世界部署验证了这些发现，所提出的策略成功引导了一台低成本机器人在反应式探索任务中运行。此外，我们通过严格的计算估算验证了模型的效率，确认其适用于资源受限的嵌入式平台，如Raspberry Pi 4和NVIDIA Jetson Nano。这项工作为开发低成本机器人系统的高效导航策略提供了一种实用解决方案。
英文摘要
Vision-based autonomous navigation for low-cost robots remains a fundamental challenge, primarily due to the significant gap between simulated training environments and real-world operational conditions. Direct policy transfer from simulation is often ineffective, while training exclusively on real data is impractical. We propose a hybrid transfer learning framework that effectively bridges the sim-to-real gap by combining domain randomization with feature-level domain adaptation. Our method employs a dual convolutional variational autoencoder architecture with a shared decoder, trained on an extensive set of 45225 simulated images and a minimal set of only 4556 real-world samples. This architecture learns a compact, common latent representation space that aligns the distributions of both domains. The adaptation process is further enhanced by two complementary data augmentation techniques designed to expand the limited real-world data. Experimental evaluation demonstrates that our method achieves an average success rate of almost 91% on image classification tasks for real-world indoor navigation, significantly outperforming both simulation-only and real-world-only training. We validate these findings through a direct, real-world deployment, where the proposed policy successfully guides a low-cost robot in a reactive exploration task. Furthermore, we validate the model's efficiency through a rigorous computational estimation, confirming its suitability for resource-constrained embedded platforms such as the Raspberry Pi 4 and NVIDIA Jetson Nano. This work presents a practical solution for developing effective and efficient navigation policies for low-cost robotic systems.
Comments30 pages, 13 figures. Published in Image and Vision Computing under a CC BY 4.0 license
Journal refImage and Vision Computing, Vol. 174, 2026, 106121
DOI：10.1016/j.imavis.2026.106121
