Combining Deep Learning and Robotics for Automated Concrete Delamination Assessment

2019 ◽  
Author(s):  
Evan McLaughlin ◽  
Nicholas Charron ◽  
Sriram Narasimhan
Keyword(s):  
Deep Learning ◽  
And Robotics

scholarly journals SeDAR: Reading Floorplans Like a Human—Using Deep Learning to Enable Human-Inspired Localisation

2019 ◽  
Vol 128 (5) ◽  
pp. 1286-1310 ◽  
Author(s):  
Oscar Mendez ◽  
Simon Hadfield ◽  
Nicolas Pugeault ◽  
Richard Bowden
Keyword(s):  
Deep Learning ◽  
Semantic Information ◽  
State Of The Art ◽  
Depth Information ◽  
Semantic Maps ◽  
Novel Method ◽  
Rgb Images ◽  
High Level ◽  
Robotic Tasks ◽  
And Robotics

Abstract The use of human-level semantic information to aid robotic tasks has recently become an important area for both Computer Vision and Robotics. This has been enabled by advances in Deep Learning that allow consistent and robust semantic understanding. Leveraging this semantic vision of the world has allowed human-level understanding to naturally emerge from many different approaches. Particularly, the use of semantic information to aid in localisation and reconstruction has been at the forefront of both fields. Like robots, humans also require the ability to localise within a structure. To aid this, humans have designed high-level semantic maps of our structures called floorplans. We are extremely good at localising in them, even with limited access to the depth information used by robots. This is because we focus on the distribution of semantic elements, rather than geometric ones. Evidence of this is that humans are normally able to localise in a floorplan that has not been scaled properly. In order to grant this ability to robots, it is necessary to use localisation approaches that leverage the same semantic information humans use. In this paper, we present a novel method for semantically enabled global localisation. Our approach relies on the semantic labels present in the floorplan. Deep Learning is leveraged to extract semantic labels from RGB images, which are compared to the floorplan for localisation. While our approach is able to use range measurements if available, we demonstrate that they are unnecessary as we can achieve results comparable to state-of-the-art without them.

A Study on Role of A.I. and Robotics in Health Care Sector: Its Impact and Contribution

2021 ◽  
Vol 07 (3&4) ◽  
pp. 7-14
Author(s):  
Devnath Jayaswal ◽  
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Health Care Sector ◽  
Healthcare Sector ◽  
Current Scenario ◽  
Health Complications ◽  
And Robotics ◽  
Major Domain

Health Care is one of the major domain sectors of our country. As this domain has many different aspect of implementation, as per the current scenario of Diseases and health complications. This paper will discuss about how, the Artificial Intelligence (A.I.) and robotics can be beneficial and plays a major role on, health care domain with respect to the Efficiently Diagnose, Developing New Medicines, Earlier Detection of Diseases, Advance Treatment Care, A.I-Deep learning For the Critical Decision’s. As this Information will help to give more clarity on what, A.I. & Robotics contributes for the major Diseases Treatment by the advancement of Technology. This can be beneficial for not only Doctors, Patients, or Firm but can also be helpful for citizen people as well. The objective of this paper is to study the role of AI and Robotics in Healthcare Sector and its impact.

scholarly journals Autonomous development and learning in artificial intelligence and robotics: Scaling up deep learning to human-like learning

2017 ◽  
Vol 40 ◽  
Author(s):  
Pierre-Yves Oudeyer
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Social Learning ◽  
Intrinsic Motivation ◽  
Scaling Up ◽  
Learning Systems ◽  
Natural Interaction ◽  
Autonomous Choice ◽  
Autonomous Development ◽  
And Robotics

AbstractAutonomous lifelong development and learning are fundamental capabilities of humans, differentiating them from current deep learning systems. However, other branches of artificial intelligence have designed crucial ingredients towards autonomous learning: curiosity and intrinsic motivation, social learning and natural interaction with peers, and embodiment. These mechanisms guide exploration and autonomous choice of goals, and integrating them with deep learning opens stimulating perspectives.

scholarly journals Machine Learning Vs Deep Learning: Which Is Better For Human Activity Recognition

2020 ◽  
Vol 9 (4) ◽  
pp. 1344-1349
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Confusion Matrix ◽  
Human Activity Recognition ◽  
Future Research ◽  
Sport Training ◽  
Upward Moving ◽  
And Robotics

Human activity recognition(HAR) is used to describe basic activities that humans are performing using the sensors that we have in smartphones. The data for this activity recognition is captured by various sensors of mobile phones or wristbands such as accelerometer, gyroscope and gravity sensors.HAR has grabbed the attention of various researchers due to its vast demand in the fields of sport training, security, entertainment health monitoring,computer vision and robotics. In this project we compare different machine learning and deep learning algorithms to find a better approach for HAR. The dataset comprises six activities i.e. walking, sleeping, sitting,moving upward, moving downwards and standing.In this demonstration we also showed confusion matrix,accuracy and multi log loss of various algorithms. With the help of accuracy, confusion matrix of algorithms we compare and determine the best approach for HAR. This will help in future research to map the activities of humans using one of the best approaches used

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Computational Vision, a Link Between Psychology and Robotics

1986 ◽  
Vol 31 (11) ◽  
pp. 872-874
Author(s):  
Bela Julesz
Keyword(s):  
Computational Vision ◽  
And Robotics
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