Fast and Accurate Tactile Object Recognition using a Random Convolutional Kernel Transform

The task of tactile object recognition is an ever-evolving research area comprising of the gathering and processing of features related to the physical interaction between a robotic system and an object or material. For a robotic system to be capable of interacting with the real-world, the ability to identify the object it is interacting with in real-time is required. Information about the object is often strongly enhanced using tactile sensing. Recent advancements in time series classifiers have allowed for the accuracy of real-time tactile object recognition to be improved, therefore generating opportunities for enhanced solutions within this field of robotics. In this paper, improvements are proposed to the state-of-the-art time series classifier ROCKET for analysis of tactile data for the purposes of object recognition. A variety of classifier heads are implemented within the ROCKET pipeline; these models are then trained and tested on the PHAC-2 tactile dataset, achieving state-of-the-art performance of 96.3% for single-modality tactile object recognition while only requiring 11 minutes to train

組み込みシステムにおける画像分類のためのマルチトリムネットワーク構造を用いたモデル圧縮

Much effort has gone into developing smart robots, wherein perception and manipulation are among the most fundamental and challenging problems. Embedded systems (ESs) are critical in robot composition. However, as an embedded system, a robot brain has a fixed resource budget and is unsuitable for modern convolutional neural networks (CNNs). Thus, the approach of CNN compression plays an important role in reducing their computational cost to make a suitable model for embedded systems. Recently, CNN compression approaches can be categorized into two groups, namely hand-crafted and model compression (MC) approach. The hand-crafted approach involves factorization and manual compression, but it is time consuming and usually requires significant amounts of manual effort and domain knowledge. Instead, the MC approach takes advantage of pre-trained models and it can solve a hand-crafted problem. The MC squeezes an existing model into one that is smaller and requires less computation. Although most MC methods can achieve a low latency or high accuracy, they are non-optimum accuracy–latency trade-off, complex, and do not affect certain dimensions (e.g., the width, resolution, and depth) of the models. To overcome this problem, the thesis presents a simple model-compression approach that optimize the accuracy–latency trade-off of the model. The multi-trimmed network structure (MTNS) is a robust combination of model compression (MC) techniques providing a lightweight model with trade-off optimization. The thesis describes a number of significant advances. Firstly, a new simple and efficient MC technique is introduced, which takes into width, resolution and depth compression. Secondly, a new multi-objective function is devised, which uses the accuracy–latency trade-off of compressed models to optimize the performance of a target model. Thirdly, a new training-accelerator is developed, which integrates pruning of convolutional kernels into shrinking the model structure to reduce training time at compressing width dimension. Finally, a new search strategy is developed, which combines Neural Architecture Search (NAS) with shrinking the model structure to explore more-complex conditions of shrinking the model structure with a relatively short training period. In an experimental evaluation, the thesis compares the performances of the proposed MTNS approach with those of CNN filter pruning, the model quantization technique, an adaptive mixture of low-rank factorizations, and knowledge distillation. The MTNS better resolved the accuracy–latency trade-off in image classification than the modern MC methods. It will be useful and friendly to the embedded system to perform a compressed model of MTNS with the maximum trade-off, lightweight, low computation and rapid process. The outstanding of the thesis is that the model compression problems have been solved by using MTNS techniques which are simple and optimum accuracy–latency trade-off for model compression.九州工業大学博士学位論文 学位記番号：工博甲第532号 学位授与年月日：令和3年9月24日1 Introduction|2 Literature Reviews|3 Preliminary Knowledge and Technique for Model Compression|4 Shrinking Structure of Models|5 Shrinking Structure of Models with Training Accelerator|6 Trim Neural Architecture Search|7 Conclusions九州工業大学令和3年