面向触觉表征学习的视觉基础模型适用性研究
Investigating Vision Foundational Models for Tactile Representation Learning
AI总结：
针对触觉传感器异构导致的触觉表征学习泛化性差的问题，提出将异构触觉数据转换为视觉输入、用视觉基础模型实现传感器无关触觉表征学习的方法，在多基准任务上验证了其性能、鲁棒性及迁移性优势。
摘要：
触觉表征学习（TRL）赋予机器人利用触觉信息的能力，可提升其在环境感知、物体操作等任务中的性能。但触觉传感器的异构性催生了大量针对特定传感器与特定任务的学习方法，这限制了现有触觉数据集的效用，也制约了学习结果的泛化能力。本研究探索视觉基础模型在传感器无关型TRL中的适用性，通过一种简单有效的转换技术，将异构传感器的读数输入模型。该方法将TRL重构为计算机视觉（CV）问题，从而可运用各类CV技术解决TRL的特有挑战。我们使用来自4种不同触觉传感器的数据集，在多个基准任务上评估该方法。实验结果表明，该方法在任务性能、模型鲁棒性，以及跨传感器、跨任务的知识可迁移性上均有显著提升，且数据需求较低。
英文摘要：
Tactile representation learning (TRL) equips robots with the ability to leverage touch information, boosting performance in tasks such as environment perception and object manipulation. However, the heterogeneity of tactile sensors results in many sensor- and task-specific learning approaches. This limits the efficacy of existing tactile datasets, and the subsequent generalisability of any learning outcome. In this work, we investigate the applicability of vision foundational models to sensor-agnostic TRL, via a simple yet effective transformation technique to feed the heterogeneous sensor readouts into the model. Our approach recasts TRL as a computer vision (CV) problem, which permits the application of various CV techniques for tackling TRL-specific challenges. We evaluate our approach on multiple benchmark tasks, using datasets collected from four different tactile sensors. Empirically, we demonstrate significant improvements in task performance, model robustness, as well as cross-sensor and cross-task knowledge transferability with limited data requirements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
