.png){kind=icon}
返回
Robotic Material Perception Using Active Multimodal Fusion
.png){kind=icon}
PRE
AI
DOI：10.1109/TIE.2018.2878157.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Robotic material perception is an extremely important but challenging problem for industrial intelligence. The main difficulties come from the fact that the material properties are difficult to be comprehensibly evaluated by single visual, auditory, or tactile modality. Conventional multimodal fusion methods require collecting all of the multimodal information for a testing sample before the recognition. This is expensive, redundant, and may incur large latency. To tackle this problem, a new active fusion framework for the multimodal material recognition is proposed in this paper. We first adopt the adversarial learning method to obtain the modal-invariant representations to effectively bridge the gap between different modalities and then develop a reinforcement learning method for active modality selection. The developed framework and algorithms are evaluated on a publicly available dataset and show promising material recognition results. The developed framework provides an effective method for industrial material inspection.
Keyword:
Active perception
adversarial learning
material recognition
multimodal fusion
reinforcement learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.2
论文数:
1.8W
被引数:
9.8W
机构
引用论文
Adaptive Impedance Control of Human-Robot Cooperation Using Reinforcement Learning基于强化学习的人机协作自适应阻抗控制
.png){kind=icon}
PREAI
分享收藏Deep Coupling Autoencoder for Fault Diagnosis With Multimodal Sensory Data用于多模态传感数据故障诊断的深度耦合自编码器
PREAI
分享收藏
Photoperiodic Effects on Steroid Negative Feedback in Female Prairie Voles (Microtus ochrogaster)
OAAI
OAAI分享收藏分享收藏
查看更多内容