Variable Stiffness Object Recognition with Bayesian Convolutional Neural Network on a Soft Gripper
2022
会议录名称INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
ISSN2153-0858
发表状态已发表
DOIhttps://doi.org/10.1109/IROS47612.2022.9982051
摘要

From a medical standpoint, detecting the size and shape of hard inclusions hidden in soft three-dimensional objects is of great significance for early detection of cancer through palpation. Soft robots, especially soft grippers, substantially broaden robots' palpation capabilities from soft to hard materials without the assistance of a camera. We have recently introduced a CNN-Bayes approach which added a Naïve Bayes classifier to a convolutional neural network (CNN) architecture called SoftTactNet for variable stiffness object recognition on a three-finger FinRay soft gripper. SoftTactNet itself lacks uncertainty estimations though it can reach a certain level of recognition accuracy. In this paper, we further improve the framework by merging Bayes method directly into CNN architectures and build a new Bayes-SoftTactNet for object recognition. The new approach, using a prior distribution instead of point estimation, allows the network to present results with uncertainty estimates. We conduct new experiments using the same soft gripper with tactile sensor arrays to grasp different variable stiffness objects surrounded by non-different soft material and generate tactile images as dataset. The results show that our new algorithm is more efficient than the previous approach and still able to achieve higher recognition accuracy than general deterministic CNNs.

会议地点Kyoto, Japan
会议日期23-27 Oct. 2022
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收录类别EI
语种英语
来源库IEEE
文献类型会议论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/296770
专题信息科学与技术学院_硕士生
信息科学与技术学院_PI研究组_ANDRE LUIS MACEDO ROSENDO SILVA组
通讯作者Cao Jinyue
作者单位
上海科技大学
第一作者单位上海科技大学
通讯作者单位上海科技大学
第一作者的第一单位上海科技大学
推荐引用方式
GB/T 7714
Cao Jinyue,Huang Jingyi,Andre Rosendo. Variable Stiffness Object Recognition with Bayesian Convolutional Neural Network on a Soft Gripper[C],2022.
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