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A survey of research on cloud robotics and automation

B Kehoe, S Patil, P Abbeel… - IEEE Transactions on …, 2015 - ieeexplore.ieee.org

The Cloud infrastructure and its extensive set of Internet-accessible resources has potential
to provide significant benefits to robots and automation systems. We consider robots and …

保存引用被引用数: 1071 関連記事全 8 バージョン

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[PDF] arxiv.org

Resource allocation and service provisioning in multi-agent cloud robotics: A comprehensive survey

M Afrin, J **, A Rahman, A Rahman… - … Surveys & Tutorials, 2021 - ieeexplore.ieee.org

Robotic applications nowadays are widely adopted to enhance operational automation and
performance of real-world Cyber-Physical Systems (CPSs) including Industry 4.0 …

保存引用被引用数: 138 関連記事全 4 バージョン

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[PDF] science.org

Learning ambidextrous robot gras** policies

J Mahler, M Matl, V Satish, M Danielczuk, B DeRose… - Science Robotics, 2019 - science.org

Universal picking (UP), or reliable robot gras** of a diverse range of novel objects from
heaps, is a grand challenge for e-commerce order fulfillment, manufacturing, inspection, and …

保存引用被引用数: 629 関連記事全 9 バージョン

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[PDF] thecvf.com

Sim-to-real via sim-to-sim: Data-efficient robotic gras** via randomized-to-canonical adaptation networks

S James, P Wohlhart, M Kalakrishnan… - Proceedings of the …, 2019 - openaccess.thecvf.com

Real world data, especially in the domain of robotics, is notoriously costly to collect. One way
to circumvent this can be to leverage the power of simulation to produce large amounts of …

保存引用被引用数: 556 関連記事全 9 バージョン HTMLバージョン

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[PDF] arxiv.org

Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics

J Mahler, J Liang, S Niyaz, M Laskey, R Doan… - ar** with deep learning and large-scale data collection

S Levine, P Pastor, A Krizhevsky… - … journal of robotics …, 2018 - journals.sagepub.com

We describe a learning-based approach to hand-eye coordination for robotic gras** from
monocular images. To learn hand-eye coordination for gras**, we trained a large …

保存引用被引用数: 2888 関連記事全 12 バージョン

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[PDF] mit.edu

Glimpse: Continuous, real-time object recognition on mobile devices

TYH Chen, L Ravindranath, S Deng, P Bahl… - Proceedings of the 13th …, 2015 - dl.acm.org

Glimpse is a continuous, real-time object recognition system for camera-equipped mobile
devices. Glimpse captures full-motion video, locates objects of interest, recognizes and …

保存引用被引用数: 591 関連記事全 13 バージョン

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[PDF] berkeley.edu

Dex-net 1.0: A cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards

J Mahler, FT Pokorny, B Hou… - … on robotics and …, 2016 - ieeexplore.ieee.org

This paper presents the Dexterity Network (Dex-Net) 1.0, a dataset of 3D object models and
a sampling-based planning algorithm to explore how Cloud Robotics can be used for robust …

保存引用被引用数: 449 関連記事全 10 バージョン

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[PDF] arxiv.org

Vision-based multi-task manipulation for inexpensive robots using end-to-end learning from demonstration

R Rahmatizadeh, P Abolghasemi… - … on robotics and …, 2018 - ieeexplore.ieee.org

We propose a technique for multi-task learning from demonstration that trains the controller
of a low-cost robotic arm to accomplish several complex picking and placing tasks, as well …

保存引用被引用数: 312 関連記事全 6 バージョン

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A survey of research on cloud robotics and automation

Resource allocation and service provisioning in multi-agent cloud robotics: A comprehensive survey

Learning ambidextrous robot gras** policies

Sim-to-real via sim-to-sim: Data-efficient robotic gras** via randomized-to-canonical adaptation networks

Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics

Glimpse: Continuous, real-time object recognition on mobile devices

Dex-net 1.0: A cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards

Vision-based multi-task manipulation for inexpensive robots using end-to-end learning from demonstration