Výsledky vyhledávání - "Hadi Beik-mohammadi"

Akademický článek

Exodex Adam—A Reconfigurable Dexterous Haptic User Interface for the Whole Hand

Autor: Neal Y. Lii, Aaron Pereira, Julian Dietl, Georg Stillfried, Annika Schmidt, Hadi Beik-Mohammadi, Thomas Baker, Annika Maier, Benedikt Pleintinger, Zhaopeng Chen, Amal Elawad, Lauren Mentzer, Austin Pineault, Philipp Reisich, Alin Albu-Schäffer

Publikováno v: Frontiers in Robotics and AI, Vol 8 (2022)

Applications for dexterous robot teleoperation and immersive virtual reality are growing. Haptic user input devices need to allow the user to intuitively command and seamlessly “feel” the environment they work in, whether virtual or a remote site

Externí odkaz: https://doaj.org/article/7193cb6b61c54ac39670f1a2aeb39b61

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Akademický článek

KidVO: a kinodynamically consistent algorithm for online motion planning in dynamic environments

Autor: Mostafa Mahmoodi, Khalil Alipour, Hadi Beik Mohammadi

Publikováno v: Industrial Robot: An International Journal, 2016, Vol. 43, Issue 1, pp. 33-47.

Externí odkaz: http://www.emeraldinsight.com/doi/10.1108/IR-05-2015-0096

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Exodex Adam-A Reconfigurable Dexterous Haptic User Interface for the Whole Hand

Autor: Neal Y, Lii, Aaron, Pereira, Julian, Dietl, Georg, Stillfried, Annika, Schmidt, Hadi, Beik-Mohammadi, Thomas, Baker, Annika, Maier, Benedikt, Pleintinger, Zhaopeng, Chen, Amal, Elawad, Lauren, Mentzer, Austin, Pineault, Philipp, Reisich, Alin, Albu-Schäffer

Publikováno v: Frontiers in robotics and AI. 8

Applications for dexterous robot teleoperation and immersive virtual reality are growing. Haptic user input devices need to allow the user to intuitively command and seamlessly "feel" the environment they work in, whether virtual or a remote site thr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::5e2bd0009f5b9ac400f8022f0021d70e
https://pubmed.ncbi.nlm.nih.gov/35309724

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Learning Riemannian Manifolds for Geodesic Motion Skills

Autor: Leonel Rozo, Søren Hauberg, Hadi Beik-mohammadi, Gerhard Neumann, Georgios Arvanitidis

Publikováno v: Robotics: Science and Systems (R:SS)
Robotics: Science and Systems XVII
Beik-Mohammadi, H, Hauberg, S, Arvanitidis, G, Neumann, G & Rozo, L 2021, Learning Riemannian Manifolds for Geodesic Motion Skills . in Proceedings of Robotics: Science and Systems 2021 . Robotics: Science and System Xvii, Robotics: Science and Systems, 12/07/2021 . https://doi.org/10.15607/rss.2021.xvii.082
Robotics: Science and Systems

*For robots to work alongside humans and perform in unstructured environments, they must learn new motion skills and adapt them to unseen situations on the fly. This demands learning models that capture relevant motion patterns, while offering enough

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3a1513613013a13bd7d288a4db993ae

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Model Mediated Teleoperation with a Hand-Arm Exoskeleton in Long Time Delays Using Reinforcement Learning

Autor: Thomas Hulin, Annika Schmidt, Philipp Reisich, Benedikt Pleintinger, Aaron Pereira, Hadi Beik-Mohammadi, Stefan Wermter, Neal Y. Lii, Matthias Kerzel

Publikováno v: RO-MAN

Telerobotic systems must adapt to new environmental conditions and deal with high uncertainty caused by long-time delays. As one of the best alternatives to human-level intelligence, Reinforcement Learning (RL) may offer a solution to cope with these

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cd4649eed90c5365427472a63f7a5c53
https://elib.dlr.de/139975/

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Designing a Personality-Driven Robot for a Human-Robot Interaction Scenario

Autor: Daniel Speck, Hadi Beik Mohammadi, Stefan Heinrich, Nikoletta Xirakia, Cornelius Weber, Irina Barykina, Stefan Wermter, Krishnan Chandran, Gitanjali Nair, Cuong Nguyen, Erik Strahl, Sascha Griffiths, Fares Abawi, Tayfun Alpay

Publikováno v: ICRA

In this paper, we present an autonomous AI system designed for a Human-Robot Interaction (HRI) study, set around a dice game scenario. We conduct a case study to answer our research question: Does a robot with a socially engaged personality lead to a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7460660c8e97ff871a4463d24a398ff6
https://doi.org/10.1109/icra.2019.8793770

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Mixed-Reality Deep Reinforcement Learning for a Reach-to-grasp Task

Autor: Mohammad Ali Zamani, Hadi Beik Mohammadi, Matthias Kerzel, Stefan Wermter

Publikováno v: Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation ISBN: 9783030304867
ICANN (1)

Deep Reinforcement Learning (DRL) has become successful across various robotic applications. However, DRL methods are not sample-efficient and require long learning times. We present an approach for online continuous deep reinforcement learning for a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d57ef1cd78267061735cacd929634d0e
https://doi.org/10.1007/978-3-030-30487-4_47

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KidVO: a kinodynamically consistent algorithm for online motion planning in dynamic environments

Autor: Khalil Alipour, Hadi Beik Mohammadi, Mostafa Mahmoodi

Publikováno v: Industrial Robot: An International Journal. 43:33-47

Purpose– The purpose of this paper is to propose an efficient method, called kinodynamic velocity obstacle (KidVO), for motion planning of omnimobile robots considering kinematic and dynamic constraints (KDCs).Design/methodology/approach– The sug

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a1d0bdbc1e13f85e7b74e8bb88897748
https://doi.org/10.1108/ir-05-2015-0096

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Real-Time Safe Navigation in Crowded Dynamic Environments Using Generalized Velocity Obstacles

Autor: Khalil Alipour, Hadi Beik Mohammadi, Mostafa Mahmoodi, Mehdi Tale Masouleh

Publikováno v: Volume 5B: 38th Mechanisms and Robotics Conference.

This paper aims at developing a real-time, robust, and reliable navigation method for an omnidirectional robot, the so-called MRL-SSL RoboCup robot, can be used in crowded dynamically-changing environments. To this end, a local motion planner will be

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9546f4764d532484f69625cc9428d058
https://doi.org/10.1115/detc2014-35036

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Accelerating Deep Continuous Reinforcement Learning through Task Simplification

Autor: Mohammad Ali Zamani, Hadi Beik Mohammadi, Stefan Wermter, Matthias Kerzel

Publikováno v: 2018 International Joint Conference on Neural Networks (IJCNN)
IJCNN

Robotic motor policies can, in theory, be learned via deep continuous reinforcement learning. In practice, however, collecting the enormous amount of required training samples in realistic time, surpasses the possibilities of many robotic platforms.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59ef4c9e8154c23aff472952dd5a656a

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