摘要
对工具及其功用性部件的认知是共融机器人智能提升的重要研究方向.本文针对家庭日常工具的功用性部件建模与检测问题展开研究,提出了一种基于条件随机场(Conditional random field, CRF)和稀疏编码联合学习的家庭日常工具功用性部件检测算法.首先,从工具深度图像提取表征工具功用性部件的几何特征;然后,分析CRF和稀疏编码之间的耦合关系并进行公式化表示,将特征稀疏化后作为潜变量构建初始条件随机场模型,并进行稀疏字典和CRF的协同优化:一方面,将特征的稀疏表示作为CRF的随机变量条件及权重参数选择器;另一方面,在CRF调控下对稀疏字典进行更新.随后使用自适应时刻估计(Adaptive moment estimation, Adam)方法实现模型解耦与求解.最后,给出了基于联合学习的工具功用性部件模型离线构建算法,以及基于该模型的在线检测方法.实验结果表明,相较于使用传统特征提取和模型构建方法,本文方法对功用性部件的检测精度和效率均得到提升,且能够满足普通配置机器人对工具功用性认知的需要.
The research for coherent robots to cognize tools and their affordance parts is an important direction to improve their machine intelligence. Aimed at modeling and detecting affordance parts of household tools, a joint learning algorithm for affordance parts detection via both conditional random field(CRF) and sparse coding is proposed. Firstly,geometric features of affordance parts are obtained from depth images of the tools. Secondly, the coupled relationship between CRF and sparse coding is analyzed and described with formulations. Initial CRF model is built by using sparse coded features as latent variables, and both the sparse dictionary and CRF are optimized simultaneously. On one hand,the sparse coded features are considered as the random variable condition and the weight parameter selector of CRF, and on the other hand, sparse dictionary is updated with the modulation of CRF. Then the model is decoupled and solved with the adaptive moment estimation(Adam). Finally, the offline joint learning algorithm for affordance parts modeling and online detection method are given. The experimental results show that, comparing with traditional features extracting and modeling methods, both the accuracy and efficiency of our method are improved, which can satisfy the affordance cognition requirements for robots with common configurations.
作者
吴培良
隰晓珺
杨霄
孔令富
侯增广
WU Pei-Liang;XI Xiao-Jun;YANG Xiao;KONG Ling-Fu;HOU Zeng-Guang(School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004;State Key Laboratoryof Management and Control for Complex Systems, Instituteof Automation, Chinese Academy of Sciences, Beijing 100190;The Key Laboratory for Computer Virtual Technology andSystem Integration of Hebei Province, Qinhuangdao 066004)
出处
《自动化学报》
EI
CSCD
北大核心
2019年第5期985-992,共8页
Acta Automatica Sinica
基金
国家重点研发计划(2018YFB1308305)
国家自然科学基金(61305113)
中国博士后自然科学基金(2018M631620)
河北省自然科学基金(F2016203358)
燕山大学博士基金(BL18007)资助~~
关键词
功用性部件检测
深度几何特征
联合学习
条件随机场
稀疏编码
Affordance parts detection
depth geometric features
joint learning
conditional random fields(CRF)
sparse coding
