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Learning to Run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning [article]

Łukasz Kidziński, Sharada P. Mohanty, Carmichael Ong, Jennifer L. Hicks, Sean F. Carroll, Sergey Levine, Marcel Salathé, Scott L. Delp
2018 arXiv   pre-print
The challenge proved that deep reinforcement learning techniques, despite their high computational cost, can be successfully employed as an optimization method for synthesizing physiologically feasible  ...  Synthesizing physiologically-accurate human movement in a variety of conditions can help practitioners plan surgeries, design experiments, or prototype assistive devices in simulated environments, reducing  ...  The challenge was co-organized by the Mobilize Center, a National Institutes of Health Big Data to Knowledge (BD2K) Center of Excellence supported through Grant U54EB020405.  ... 
arXiv:1804.00198v1 fatcat:igfpn6joujg57l3r5yy3dj4fwe

Artificial Intelligence for Prosthetics - challenge solutions [article]

Łukasz Kidziński, Carmichael Ong, Sharada Prasanna Mohanty, Jennifer Hicks, Sean F. Carroll, Bo Zhou, Hongsheng Zeng, Fan Wang, Rongzhong Lian, Hao Tian, Wojciech Jaśkowski, Garrett Andersen, Odd Rune Lykkebø, Nihat Engin Toklu (+31 others)
2019 arXiv   pre-print
Top participants were invited to describe their algorithms. In this work, we describe the challenge and present thirteen solutions that used deep reinforcement learning approaches.  ...  imitation learning.  ...  Participants were provided with a human musculoskeletal model and a physics-based simulation environment (OpenSim [9, 42] ) in which they synthesized physically and physiologically accurate motion (  ... 
arXiv:1902.02441v1 fatcat:hf7xzitrhjdqfb5cfaneovlfa4

Deep learning for smart fish farming: applications, opportunities and challenges [article]

Xinting Yang, Song Zhang, Jintao Liu, Qinfeng Gao, Shuanglin Dong, Chao Zhou
2020 arXiv   pre-print
With the rapid emergence of deep learning (DL) technology, it has been successfully used in various fields including aquaculture.  ...  However, challenges still exist; DL is still in an era of weak artificial intelligence.  ...  Concepts of deep learning Terms and definitions of deep learning Deep learning is a branch of machine learning.  ... 
arXiv:2004.11848v1 fatcat:jzdndxhoxvdafos7qfbol3tcqa

Digital Twin: Values, Challenges and Enablers [article]

Adil Rasheed, Omer San, Trond Kvamsdal
2019 arXiv   pre-print
Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.  ...  In this work, we review the recent status of methodologies and techniques related to the construction of digital twins.  ...  ACKNOWLEDGMENT The authors would like to thank Professor Harald Martens for his helpful comments and suggestions on an earlier draft of this manuscript.  ... 
arXiv:1910.01719v1 fatcat:3r2bbivffbeizelake7oo3xgbm

Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation [article]

Seungmoon Song, Łukasz Kidziński, Xue Bin Peng, Carmichael Ong, Jennifer L. Hicks, Serge Levine, Christopher Atkeson, Scot Delp
2020 bioRxiv   pre-print
Top teams adapted state-of-art deep reinforcement learning techniques to produce complex motions, such as quick turning and walk-to-stand transitions, that have not been demonstrated before in neuromechanical  ...  We also present a scientific competition and accompanying software platform, which we have organized to accelerate the use of reinforcement learning in neuromechanical simulations.  ...  Delp, 570 Learning to run challenge: Synthesizing physiologically accurate motion using deep reinforcement 571 learning, in: The NIPS'17 Competition: Building Intelligent Systems, Springer, 2018, pp. ]  ... 
doi:10.1101/2020.08.11.246801 fatcat:qjkr2kn3hnehppsp7fnzsqz34m

Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving [article]

Junyao Guo, Unmesh Kurup, Mohak Shah
2018 arXiv   pre-print
Specifically, we categorize the datasets according to use cases, and highlight the datasets that capture complicated and hazardous driving conditions which can be better used for training robust driving  ...  Also, one of the main mechanisms to adapt autonomous driving systems to any driving condition is to be able to learn and generalize from representative scenarios.  ...  ACKNOWLEDGEMENT The authors would like to thank Prof. Maxim Likhachev from Carnegie Mellon University for his invaluable comments that improved the manuscript.  ... 
arXiv:1811.11277v1 fatcat:ztrxyydtuveijizfn6a2dmt5ui

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi
2021 Information Fusion  
This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlights fundamental research challenges and directions  ...  Bayesian approximation and ensemble learning techniques are two widely-used types of uncertainty quantification (UQ) methods.  ...  In this regard, they proposed a robust and accurate deep learning (CNN) segmentation model.  ... 
doi:10.1016/j.inffus.2021.05.008 fatcat:yschhguyxbfntftj6jv4dgywxm

Digital Twin: Values, Challenges and Enablers from a Modeling Perspective

Adil Rasheed, Omer San, Trond Kvamsdal
2020 IEEE Access  
Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.  ...  INDEX TERMS Digital twin, artificial intelligence, machine learning, big data cybernetics, hybrid analysis and modeling. 21980 This work is licensed under a Creative Commons Attribution 4.0 License.  ...  This is inspired from the deep reinforcement learning with gradient acting as the policy function.  ... 
doi:10.1109/access.2020.2970143 fatcat:nmdakwa2urdmbbcjblsjrkypum

Foundations and Recent Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions [article]

Paul Pu Liang, Amir Zadeh, Louis-Philippe Morency
2022 arXiv   pre-print
learning has brought unique computational and theoretical challenges to the machine learning community given the heterogeneity of data sources and the interconnections often found between modalities.  ...  Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design computer agents with intelligent capabilities such as understanding, reasoning, and learning through integrating  ...  PPL is partially supported by a Facebook PhD Fellowship and a Carnegie Mellon University's Center for Machine Learning and Health Fellowship.  ... 
arXiv:2209.03430v1 fatcat:ne5vfyz67rgxzmvdiv37hpuare

Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Technical Challenges and Solutions [article]

Eike Petersen, Yannik Potdevin, Esfandiar Mohammadi, Stephan Zidowitz, Sabrina Breyer, Dirk Nowotka, Sandra Henn, Ludwig Pechmann, Martin Leucker, Philipp Rostalski, Christian Herzog
2021 arXiv   pre-print
Important measures that may help to address these challenges include the use of large and representative datasets and federated learning as a means to that end, the careful exploitation of domain knowledge  ...  classical black-box deep neural networks.  ...  Acknowledgements The authors would like to thank Jannik Prüßmann, B.Sc., for assisting with the formatting of the references.  ... 
arXiv:2107.09546v1 fatcat:er3nlre7xrg4lmqsgxs7c4pswu

Discovering Diverse Athletic Jumping Strategies [article]

Zhiqi Yin, Zeshi Yang, Michiel van de Panne, KangKang Yin
2021 arXiv   pre-print
Given a task objective and an initial character configuration, the combination of physics simulation and deep reinforcement learning (DRL) provides a suitable starting point for automatic control policy  ...  To facilitate the learning of realistic human motions, we propose a Pose Variational Autoencoder (P-VAE) to constrain the actions to a subspace of natural poses.  ...  ACKNOWLEDGMENTS We would like to thank the anonymous reviewers for their constructive feedback. We thank Beyond Capture for helping us with motion capture various high jumps.  ... 
arXiv:2105.00371v1 fatcat:ualaqxtku5ebpctdpmn5hienw4

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Nora El-Rashidy, Shaker El-Sappagh, S. M. Riazul Islam, Hazem M. El-Bakry, Samir Abdelrazek
2021 Diagnostics  
, its challenges, and its probable future directions.  ...  RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians.  ...  Others use reinforcement learning to find the optimal treatment for a patient with anemia. (3) Deep learning (DL): This is a new area of ML that simulates the human thinking process.  ... 
doi:10.3390/diagnostics11040607 pmid:33805471 pmcid:PMC8067150 fatcat:a42vma6mlfan3cqs5z7e2bs47a

Sample Efficient Ensemble Learning with Catalyst.RL [article]

Sergey Kolesnikov, Valentin Khrulkov
2020 arXiv   pre-print
To demonstrate the effectiveness of Catalyst.RL, we applied it to a physics-based reinforcement learning challenge "NeurIPS 2019: Learn to Move -- Walk Around" with the objective to build a locomotion  ...  We present Catalyst.RL, an open-source PyTorch framework for reproducible and sample efficient reinforcement learning (RL) research.  ...  ., 2018] ) in which they synthesized physically and physiologically accurate motion.  ... 
arXiv:2003.14210v2 fatcat:pz6inzamjjdpdb62weovxhkj5a

Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions

Eike Petersen, Yannik Potdevin, Esfandiar Mohammadi, Stephan Zidowitz, Sabrina Breyer, Dirk Nowotka, Sandra Henn, Ludwig Pechmann, Martin Leucker, Philipp Rostalski, Christian Herzog
2022 IEEE Access  
solutions to address these challenges.  ...  Promising solution approaches include the use of large and representative datasets and federated learning as a means to that end, the careful exploitation of domain knowledge, the use of inherently transparent  ...  ACKNOWLEDGMENT The authors would like to thank Jannik Prüßmann, B.Sc., for assistance with the formatting of the references.  ... 
doi:10.1109/access.2022.3178382 fatcat:cwpkgkx2ibcgbdatd4aidwa4xy

Challenges and solutions for application and wider adoption of wearable robots

Jan Babič, Matteo Laffranchi, Federico Tessari, Tom Verstraten, Domen Novak, Nejc Šarabon, Barkan Ugurlu, Luka Peternel, Diego Torricelli, Jan F. Veneman
2021 Wearable Technologies  
The science and technology of wearable robots are steadily advancing, and the use of such robots in our everyday life appears to be within reach.  ...  The aim of this article is to address the current challenges that are limiting the application and wider adoption of wearable robots that are typically worn over the human body.  ...  Alternatively, reinforcement learning can be applied to optimize learned trajectories based on some specific metrics while the user performs the task (Huang et al., 2016) .  ... 
doi:10.1017/wtc.2021.13 fatcat:7updekbfbjdk7mxskg276shlmq
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