Advancing the incremental fusion of robotic sensory features using online multi-kernel extreme learning machine

Cao, Lele; Sun, Fuchun; Li, Hongbo; Huang, Wenbing

doi:10.1007/s11704-016-5171-9

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…Google announced its driverless car project in 2010, and released a video, in which its one of the employees Steve Mahan who lost 95% of the vision, safely drove 12 miles [5]. Ding et al proposed optimisation extreme learning machine method of the SMO, however, owing to the large data set optimal parameters C , the algorithm requires more iterations to converge to the optimal solution of the optimisation problem [6]. In addition, teaching by demonstration (TbD) technique, the conception of which is demonstrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

Segmentation and generalisation for writing skills transfer from humans to robots

Yang

Giannetti

2019

Cognitive Computation and Systems

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In this study, the authors present an enhanced generalised teaching by demonstration technique for a KUKA iiwa robot. Movements are recorded from a human operator, and then the recorded data are sent to be segmented via MATLAB by using the difference method (DV). The outputted trajectories data are used to model a non-linear system named dynamic movement primitive (DMP). For the purpose of learning from multiple demonstrations correctly and accurately, the Gaussian mixture model is employed for the evaluation of the DMP in order to modelling multiple trajectories by the teaching of demonstrator. Furthermore, a synthesised trajectory with smaller position errors in 3D space has been successfully generated by the usage of the Gaussian mixture regression algorithm. The proposed approach has been tested and demonstrated by performing a Chinese characters writing task with a KUKA iiwa robot.

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Section: Introductionmentioning

confidence: 99%

Segmentation and generalisation for writing skills transfer from humans to robots

Yang

Giannetti

2019

Cognitive Computation and Systems

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Efficient Adaptive Online Learning via Frequent Directions

Wan

Zhang

2022

IEEE Trans. Pattern Anal. Mach. Intell.

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Novel Approach for Robotic Process Automation with Increasing Productivity and Improving Product Quality using Machine Learning

Jha¹,

Upadhyay²

2021

IJEAT

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Robotic Process Automation (RPA) is one of the smartest technology evolutions in recent years. It is, a software installed on a system. RPA can be implemented in a well-defined environment with defined procedures and clarity with reference to decision making. RPA’s limitation is that it cannot be automated if it involves decision making supported by knowledgebased application. Highly invasive and intertwined supply chains are now confronted by producers, which reduce manufacturing life cycles and raise product sophistication. You therefore sense the need, at all stages of value formation, to change and adjust more rapidly. The theory of self-optimization is a positive method to coping with uncertainty and unexpected delays within supply chains, devices and processes. It would also boost manufacturing industries' stability and productivity. This paper explores the idea of development processes that are self-optimized. Following a quick historical analysis and understanding the particular needs, specifications and self-optimizing criteria of the various stages of value generation from supply chain planning and management to manufacture and assembly. Examples at both stages are used to demonstrate the self-optimization principle and to explain its simplicity and efficiency ability.. We proposed Novel approach for Robotic Process Automation with increasing productivity and improving product quality using machine learning

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Advancing the incremental fusion of robotic sensory features using online multi-kernel extreme learning machine

Cited by 3 publications

References 28 publications

Segmentation and generalisation for writing skills transfer from humans to robots

Segmentation and generalisation for writing skills transfer from humans to robots

Efficient Adaptive Online Learning via Frequent Directions

Novel Approach for Robotic Process Automation with Increasing Productivity and Improving Product Quality using Machine Learning

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