Deep learning in robotics: a review of recent research

Pierson, Harry A.; Gashler, Michael S.

doi:10.1080/01691864.2017.1365009

Cited by 243 publications

(112 citation statements)

References 95 publications

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“…Deep learning, as an effective machine learning algorithm, has been widely studied (LeCun, Bengio, & Hinton, ) and now attracts more attentions from various fields such as remote sensing (G. Cheng & Han, ), agriculture production (Kamilaris & Prenafeta‐Boldu, ), medical science (Shen, Wu, & Suk, ), robotics (Pierson & Gashler, ), healthcare (Miotto, Wang, Wang, Jiang, & Dudley, ), human action recognition (D. Wu, Sharma, & Blumenstein, ), speech recognition (Noda, Yamaguchi, Nakadai, Okuno, & Ogata, ), and so on. Deep learning has showed significant advantages in automatically learning data representations (even for multidomain feature extraction), transfer learning (Ng, Nguyen, Vonikakis, & Winkler, ), dealing with the large amount of data, and obtaining better performance and higher precision (Kamilaris & Prenafeta‐Boldu, ).…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning in Food: A Review

Zhou

Zhang

Liu

et al. 2019

Comp Rev Food Sci Food Safe

388

166

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Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food science and engineering. To our knowledge, this review is the first in the food domain. In this paper, we provided a brief introduction of deep learning and detailedly described the structure of some popular architectures of deep neural networks and the approaches for training a model. We surveyed dozens of articles that used deep learning as the data analysis tool to solve the problems and challenges in food domain, including food recognition, calories estimation, quality detection of fruits, vegetables, meat and aquatic products, food supply chain, and food contamination. The specific problems, the datasets, the preprocessing methods, the networks and frameworks used, the performance achieved, and the comparison with other popular solutions of each research were investigated. We also analyzed the potential of deep learning to be used as an advanced data mining tool in food sensory and consume researches. The result of our survey indicates that deep learning outperforms other methods such as manual feature extractors, conventional machine learning algorithms, and deep learning as a promising tool in food quality and safety inspection. The encouraging results in classification and regression problems achieved by deep learning will attract more research efforts to apply deep learning into the field of food in the future.

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Section: Introductionmentioning

confidence: 99%

Application of Deep Learning in Food: A Review

Zhou

Zhang

Liu

et al. 2019

Comp Rev Food Sci Food Safe

388

166

View full text Add to dashboard Cite

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“…In addition to the speech, NLP, and computer vision tasks mentioned in previous sections, robotics faces a few application specific tasks of learning, reasoning, and embodiment. Some recent reviews of the state of art and discussion of its potential and limitations are available [9] [10]. One key technique needed to make progress is to enable robots to autonomously acquire skills from sensory data.…”

Section: Roboticsmentioning

confidence: 99%

Deep learning on mobile devices: a review

Deng

2019

Mobile Multimedia/Image Processing, Security, and Applications 2019

100

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Recent breakthroughs in deep learning and artificial intelligence technologies have enabled numerous mobile applications. While traditional computation paradigms rely on mobile sensing and cloud computing, deep learning implemented on mobile devices provides several advantages. These advantages include low communication bandwidth, small cloud computing resource cost, quick response time, and improved data privacy. Research and development of deep learning on mobile and embedded devices has recently attracted much attention. This paper provides a timely review of this fast-paced field to give the researcher, engineer, practitioner, and graduate student a quick grasp on the recent advancements of deep learning on mobile devices. In this paper, we discuss hardware architectures for mobile deep learning, including Field Programmable Gate Arrays (FPGA), Application Specific Integrated Circuit (ASIC), and recent mobile Graphic Processing Units (GPUs). We present Size, Weight, Area and Power (SWAP) considerations and their relation to algorithm optimizations, such as quantization, pruning, compression, and approximations that simplify computation while retaining performance accuracy. We cover existing systems and give a state-of-the-industry review of TensorFlow, MXNet, Mobile AI Compute Engine (MACE), and Paddle-mobile deep learning platform. We discuss resources for mobile deep learning practitioners, including tools, libraries, models, and performance benchmarks. We present applications of various mobile sensing modalities to industries, ranging from robotics, healthcare and multimedia, biometrics to autonomous drive and defense. We address the key deep learning challenges to overcome, including low quality data, and small training/adaptation data sets. In addition, the review provides numerous citations and links to existing code bases implementing various technologies. These resources lower the user's barrier to entry into the field of mobile deep learning.

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“…An adaptive ToM for robots would also tackle many of the challenges identified in the robotics field by Lake et al [24]. Integrating ToM development principles in the blueprint of an adapting neural architecture for social interaction may result in a more time-and cost-efficient learning process compared to DNNs [6]. Furthermore, it would decrease the need to select and feed appropriate content to robots through expensive datasets, also reducing human errors involved in their preparation and permitting increased accuracy.…”

Section: Functional Advantages For Roboticsmentioning

confidence: 99%

Transferring Adaptive Theory of Mind to Social Robots: Insights from Developmental Psychology to Robotics

Bianco

Ognibene

2019

Lecture Notes in Computer Science

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Despite the recent advancement in the social robotic field, important limitations restrain its progress and delay the application of robots in everyday scenarios. In the present paper, we propose to develop computational models inspired by our knowledge of human infants' social adaptive abilities. We believe this may provide solutions at an architectural level to overcome the limits of current systems. Specifically, we present the functional advantages that adaptive Theory of Mind (ToM) systems would support in robotics (i.e., mentalizing for belief understanding, proactivity and preparation, active perception and learning) and contextualize them in practical applications. We review current computational models mainly based on the simulation and teleological theories, and robotic implementations to identify the limitations of ToM functions in current robotic architectures and suggest a possible future developmental pathway. Finally, we propose future studies to create innovative computational models integrating the properties of the simulation and teleological approaches for an improved adaptive ToM ability in robots with the aim of enhancing human-robot interactions and permitting the application of robots in unexplored environments, such as disasters and construction sites. To achieve this goal, we suggest directing future research towards the modern cross-talk between the fields of robotics and developmental psychology.

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Deep learning in robotics: a review of recent research

Cited by 243 publications

References 95 publications

Application of Deep Learning in Food: A Review

Application of Deep Learning in Food: A Review

Deep learning on mobile devices: a review

Transferring Adaptive Theory of Mind to Social Robots: Insights from Developmental Psychology to Robotics

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