Aleksei Petrenko

Cited by

	All	Since 2019
Citations	234	234
h-index	7	7
i10-index	5	5

140

105

2019202020212022202320241 4 17 43 132 37

Co-authors

Arthur AllshireUniversity of Toronto, NVIDIAVerified email at mail.utoronto.ca
Ankur HandaPrincipal Research Scientist, NVIDIAVerified email at doc.ic.ac.uk
Gaurav SukhatmeProfessor, Departments of CS and ECE, USCVerified email at usc.edu
Viktor MakoviychukNVIDIAVerified email at nvidia.com
Vladlen KoltunDistinguished Scientist, AppleVerified email at apple.com
Zhehui HuangPh.D. student of computer science, University of Southern CaliforniaVerified email at usc.edu
Tushar KumarMaster's Student, University of Southern CaliforniaVerified email at usc.edu
Sumeet BatraUniversity of Southern CaliforniaVerified email at usc.edu
Denys MakoviichukMachine Learning Engineering ManagerVerified email at snap.com
Erik WijmansAppleVerified email at apple.com
Artem MolchanovApplied Research Scientist, NVidiaVerified email at nvidia.com
Kayvon FatahalianAssistant Professor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Dhruv BatraFAIR (Meta AI) | Georgia TechVerified email at gatech.edu
Manolis SavvaSimon Fraser UniversityVerified email at sfu.ca
Shashank HegdeUniversity of Southern CaliforniaVerified email at usc.edu
Anssi KanervistoResearcher, FAIR MetaVerified email at meta.com
Dieter FoxUniversity of Washington and NVIDIAVerified email at cs.washington.edu

Aleksei Petrenko

Research Scientist at Apple, Computer Science PhD from the University of Southern California

Verified email at apple.com - Homepage

reinforcement learning deep reinforcement learning deep learning systems robotics


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Sample factory: Egocentric 3d control from pixels at 100000 fps with asynchronous reinforcement learning A Petrenko, Z Huang, T Kumar, G Sukhatme, V Koltun International Conference on Machine Learning, 7652-7662, 2020	70	2020
Dextreme: Transfer of agile in-hand manipulation from simulation to reality A Handa, A Allshire, V Makoviychuk, A Petrenko, R Singh, J Liu, ... 2023 IEEE International Conference on Robotics and Automation (ICRA), 5977-5984, 2023	62*	2023
Decentralized control of quadrotor swarms with end-to-end deep reinforcement learning S Batra, Z Huang, A Petrenko, T Kumar, A Molchanov, GS Sukhatme Conference on Robot Learning, 576-586, 2022	36	2022
Large batch simulation for deep reinforcement learning B Shacklett, E Wijmans, A Petrenko, M Savva, D Batra, V Koltun, ... International Conference on Learning Representations, 2021, 2021	23	2021
Megaverse: Simulating embodied agents at one million experiences per second A Petrenko, E Wijmans, B Shacklett, V Koltun International Conference on Machine Learning, 8556-8566, 2021	18	2021
Dexpbt: Scaling up dexterous manipulation for hand-arm systems with population based training A Petrenko, A Allshire, G State, A Handa, V Makoviychuk arXiv preprint arXiv:2305.12127, 2023	8	2023
Agents that listen: High-throughput reinforcement learning with multiple sensory systems S Hegde, A Kanervisto, A Petrenko 2021 IEEE Conference on Games (CoG), 1-5, 2021	8	2021
Proximal policy gradient arborescence for quality diversity reinforcement learning S Batra, B Tjanaka, MC Fontaine, A Petrenko, S Nikolaidis, G Sukhatme arXiv preprint arXiv:2305.13795, 2023	5	2023
Quadswarm: A modular multi-quadrotor simulator for deep reinforcement learning with direct thrust control Z Huang, S Batra, T Chen, R Krupani, T Kumar, A Molchanov, A Petrenko, ... arXiv preprint arXiv:2306.09537, 2023	4	2023
Ai-based control for robotics systems and applications A Handa, AD Allshire, V Makoviichuk, AV Petrenko US Patent App. 18/330,905, 2024		2024
Training machine learning models using simulation for robotics systems and applications A Handa, AD Allshire, D Fox, JFV Lafleche, LIU Jingzhou, V Makoviichuk, ... US Patent App. 18/448,049, 2024		2024

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