Mining local and global spatiotemporal features for tactile object recognition

Abstract

The tactile object recognition (TOR) is highly important for environmental perception of robots. The previous works usually utilize single scale convolution which cannot simultaneously extract local and global spatiotemporal features of tactile data, which leads to low accuracy in TOR task. To address above problem, this article proposes a local and global residual (LGR-18) network which is mainly consisted of multiple local and global convolution (LGC) blocks. An LGC block contains two pairs of local convolution (LC) and global convolution (GC) modules. The LC module mainly utilizes a temporal shift operation and a 2D convolution layer to extract local spatiotemporal features. The GC module extracts global spatiotemporal features by fusing multiple 1D and 2D convolutions which can expand the receptive field in temporal and spatial dimensions. Consequently, our LGR-18 network can extract local-global spatiotemporal features without using 3D convolutions which usually require a large number of parameters. The effectiveness of LC module, GC module and LGC block is verified by ablation studies. Quantitative comparisons with state-of-the-art methods reveal the excellent capability of our method.