Multimodal Sparse Representation for Anomaly Classification in A Robot Introspection System

Wu, Hongmin; Liu, Dong; Duan, Shuangda; Guan, Yisheng; Rojas, Juan

doi:10.1109/robio.2018.8665206

Search citation statements

Order By: Relevance

Paper Sections

Select...

Related Work1

A Feature Extraction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

2022

Publication Types

Select...

Article1

Other1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [26], a deep neural network is constructed to detect the system faulty status. In [27], various classifiers are developed and tested using the recorded signal samples. Similar work also includes [28], [29], where neural network is constructed to monitor the grasping slippages and colliding torques, and [30], [31], where SVM classifiers are developed to detect external collisions.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An Online Robot Collision Detection and Identification Scheme by Supervised Learning and Bayesian Decision Theory

Zhang

Qian

Schuller

et al. 2021

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

proposed collision identification scheme in this paper especially focuses on overcoming the following two challenges in practice: firstly, to timely and accurately report a collision within its early stage; secondly, to ensure a high identification accuracy in a complicated environment, where ubiquitous disturbance and noise are unneglectable. The experimental validation at the end of this paper confirms its promising application value in humanrobot collaboration.

show abstract

Section: Related Workmentioning

confidence: 99%

“…To form a feature set that benefits the signal classification, we consider both the properties of pHRI signals and the successful experience in previous work [27], [38]. From Fig.…”

Section: A Feature Extractionmentioning

confidence: 99%