软体机器人的跨域迁移学习与状态推断：基于半监督序列变分贝叶斯框架
Cross-domain Transfer Learning and State Inference for Soft Robots via a Semi-supervised Sequential Variational Bayes Framework
AI总结：
针对软体机器人状态标签缺失问题，提出半监督序列变分贝叶斯框架DSVB，结合特征空间迁移与循环神经网络，实现跨配置的有效迁移学习和准确状态推断。
摘要：
近年来，深度神经网络等数据驱动模型已被证明是软体机器人建模和状态推断的有力工具。然而，深度模型的有效运行需要大量数据，这要求进行详尽且高质量的数据收集，尤其是状态标签的收集。因此，由于软体机器人传感器化困难以及在非结构化环境中收集数据的不便等原因，获取软体机器人系统的带标签状态数据面临挑战。为解决这一挑战，本文提出了一种半监督序列变分贝叶斯（DSVB）框架，用于在特定机器人配置下状态标签缺失的软体机器人中进行迁移学习和状态推断。考虑到软体机器人在不同配置下可能表现出不同的动力学特性，还引入了一种特征空间迁移策略，以促进潜在特征在多种配置间的适应。与现有的迁移学习方法不同，我们提出的DSVB采用循环神经网络来建模软体机器人数据中的非线性动力学和时间连贯性。该框架在气动软体机器人手指的多种设置配置上进行了验证。在四个迁移场景上的实验结果表明，DSVB在状态标签缺失的情况下能够进行有效的迁移学习和准确的状态推断。数据和代码可在https://github.com/shageenderan/DSVB获取。
英文摘要：
Recently, data-driven models such as deep neural networks have shown to be promising tools for modelling and state inference in soft robots. However, voluminous amounts of data are necessary for deep models to perform effectively, which requires exhaustive and quality data collection, particularly of state labels. Consequently, obtaining labelled state data for soft robotic systems is challenged for various reasons, including difficulty in the sensorization of soft robots and the inconvenience of collecting data in unstructured environments. To address this challenge, in this paper, we propose a semi-supervised sequential variational Bayes (DSVB) framework for transfer learning and state inference in soft robots with missing state labels on certain robot configurations. Considering that soft robots may exhibit distinct dynamics under different robot configurations, a feature space transfer strategy is also incorporated to promote the adaptation of latent features across multiple configurations. Unlike existing transfer learning approaches, our proposed DSVB employs a recurrent neural network to model the nonlinear dynamics and temporal coherence in soft robot data. The proposed framework is validated on multiple setup configurations of a pneumatic-based soft robot finger. Experimental results on four transfer scenarios demonstrate that DSVB performs effective transfer learning and accurate state inference amidst missing state labels. The data and code are available at https://github.com/shageenderan/DSVB.
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。
