This paper introduces a system of data collection acceleration and real-to-sim transferring for surface recognition on a quadruped robot. The system features a mechanical single-leg setup capable of stepping on various easily interchangeable surfaces. Additionally, it incorporates a GRU-based Surface Recognition System, inspired by the system detailed in the Dog-Surf paper. This setup facilitates the expansion of dataset collection for model training, enabling data acquisition from hard-to-reach surfaces in laboratory conditions. Furthermore, it opens avenues for transferring surface properties from reality to simulation, thereby allowing the training of optimal gaits for legged robots in simulation environments using a pre-prepared library of digital twins of surfaces. Moreover, enhancements have been made to the GRU-based Surface Recognition System, allowing for the integration of data from both the quadruped robot and the single-leg setup. The dataset and code have been made publicly available.

翻译：本文介绍了一种用于四足机器人表面识别的数据采集加速与实景-仿真迁移系统。该系统采用机械单腿装置，可踩踏多种易于更换的表面。此外，系统集成了基于门控循环单元（GRU）的表面识别系统，其设计灵感来源于Dog-Surf论文中详述的架构。该装置能够有效扩展模型训练所需的数据集采集范围，实现在实验室条件下对难以触及表面的数据获取。同时，该系统为表面特性从现实到仿真环境的迁移提供了途径，从而可利用预先构建的表面数字孪生库，在仿真环境中训练足式机器人的最优步态。此外，本研究对基于GRU的表面识别系统进行了改进，使其能够整合来自四足机器人平台与单腿装置的双源数据。相关数据集与代码已公开。