Tactile recognition aims at identifying target objects according to tactile sensory readings. Tactile data have two salient properties: 1) sequentially real-time and 2) temporally correlated, which essentially calls for a real-time (i.e., online fixed-budget) and recurrent recognition procedure. Based on an efficient and robust spatio-temporal feature representation for tactile sequences, we handle the problem of real-time recurrent tactile recognition by proposing a bounded online-sequential learning framework, and incorporates the strength of batch-regularization bootstrapping, bounded recursive reservoir, and momentum-based estimation. Experimental evaluations show that it outperforms the state-of-the-art methods by a large margin on test accuracy; and its training performance is superior to most compared models from aspects of average online training error, computational complexity, and storage efficiency.

中文翻译：

---

实时循环触觉识别：Momentum Batch-Sequential Echo State Networks

触觉识别旨在根据触觉感官读数识别目标物体。触觉数据具有两个显着特性：1) 顺序实时和 2) 时间相关，这本质上需要实时（即在线固定预算）和循环识别过程。基于触觉序列的有效且稳健的时空特征表示，我们通过提出有界在线序列学习框架来处理实时循环触觉识别问题，并结合批量正则化自举、有界递归库的优势，和基于动量的估计。实验评估表明，它在测试精度上大大优于最先进的方法；