A Survey of Knowledge Representation in Service Robotics

Paulius, David; Sun, Yu

doi:10.1016/j.robot.2019.03.005

Cited by 93 publications

(52 citation statements)

References 151 publications

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“…Thanks to the Professor Yu Sun and the T.A. David Paulius for the help with the project/understanding the functional object oriented notation language and for writing the following papers on functional units for me to look at for even more additional information [1], [2], [3], [4], [5], [6], [7].…”

Section: Acknowledgmentsmentioning

confidence: 99%

Expanding Functional Object Oriented Network and Searching Task Trees

Martin¹

2018

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Section: Acknowledgmentsmentioning

confidence: 99%

Expanding Functional Object Oriented Network and Searching Task Trees

Martin¹

2018

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“…As an important carrier of artificial intelligence, how to design intelligent robots has become an active research subject. With the continuous improvement of people's living standards, more and more service robots have been employed in people's lives [5]. For instance, in large shopping malls, service robots are adopted as an assistant for shopping, thereby improving the customers' service efficiency [6,7].…”

Section: Introductionmentioning

confidence: 99%

Designing a Supermarket Service Robot Based on Deep Convolutional Neural Networks

et al. 2020

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In order to save people’s shopping time and reduce labor cost of supermarket operations, this paper proposes to design a supermarket service robot based on deep convolutional neural networks (DCNNs). Firstly, according to the shopping environment and needs of supermarket, the hardware and software structure of supermarket service robot is designed. The robot uses a robot operating system (ROS) middleware on Raspberry PI as a control kernel to implement wireless communication with customers and staff. So as to move flexibly, the omnidirectional wheels symmetrically installed under the robot chassis are adopted for tracking. The robot uses an infrared detection module to detect whether there are commodities in the warehouse or shelves or not, thereby grasping and placing commodities accurately. Secondly, the recently-developed single shot multibox detector (SSD), as a typical DCNN model, is employed to detect and identify objects. Finally, in order to verify robot performance, a supermarket environment is designed to simulate real-world scenario for experiments. Experimental results show that the designed supermarket service robot can automatically complete the procurement and replenishment of commodities well and present promising performance on commodity detection and recognition tasks.

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“…With such strings, we can also consolidate aliases or terms for different motions (even in other languages) since they will be represented in a format that describes the motions on a functional level. A properly represented motion in a machine language is crucial for manipulation knowledge representation [14] such as functional object-oriented network (FOON) [15]. Using our proposed motion taxonomy, motions with different names such as "insert" and "pierce" are represented with the same manipulation code, as they share the same motion and tactile features in the taxonomy.…”

Section: Introductionmentioning

confidence: 99%

Manipulation Motion Taxonomy and Coding for Robots

Paulius

Huang

Meloncon

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper introduces a taxonomy of manipulations as seen especially in cooking for 1) grouping manipulations from the robotics point of view, 2) consolidating aliases and removing ambiguity for motion types, and 3) provide a path to transferring learned manipulations to new unlearned manipulations. Using instructional videos as a reference, we selected a list of common manipulation motions seen in cooking activities grouped into similar motions based on several trajectory and contact attributes. Manipulation codes are then developed based on the taxonomy attributes to represent the manipulation motions. The manipulation taxonomy is then used for comparing motion data in the Daily Interactive Manipulation (DIM) data set to reveal their motion similarities.

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A Survey of Knowledge Representation in Service Robotics

Cited by 93 publications

References 151 publications

Expanding Functional Object Oriented Network and Searching Task Trees

Expanding Functional Object Oriented Network and Searching Task Trees

Designing a Supermarket Service Robot Based on Deep Convolutional Neural Networks

Manipulation Motion Taxonomy and Coding for Robots

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