Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDAravind Rajeswaran
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2022
[c24]Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, Abhinav Gupta:
R3M: A Universal Visual Representation for Robot Manipulation. CoRL 2022: 892-909- [c23]Simone Parisi, Aravind Rajeswaran, Senthil Purushwalkam, Abhinav Gupta:
The Unsurprising Effectiveness of Pre-Trained Vision Models for Control. ICML 2022: 17359-17371 - [c22]Tanmay Shankar, Yixin Lin, Aravind Rajeswaran, Vikash Kumar, Stuart Anderson, Jean Oh:
Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots. ICML 2022: 19626-19644 - [c21]Yuchen Cui, Scott Niekum, Abhinav Gupta, Vikash Kumar, Aravind Rajeswaran:
Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation? L4DC 2022: 893-905 - [i31]Michael Laskin, Hao Liu, Xue Bin Peng, Denis Yarats, Aravind Rajeswaran, Pieter Abbeel:
CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery. CoRR abs/2202.00161 (2022) - [i30]Simone Parisi, Aravind Rajeswaran, Senthil Purushwalkam, Abhinav Gupta:
The Unsurprising Effectiveness of Pre-Trained Vision Models for Control. CoRR abs/2203.03580 (2022) - [i29]Allan Zhou, Vikash Kumar, Chelsea Finn, Aravind Rajeswaran:
Policy Architectures for Compositional Generalization in Control. CoRR abs/2203.05960 (2022) - [i28]Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, Abhinav Gupta:
R3M: A Universal Visual Representation for Robot Manipulation. CoRR abs/2203.12601 (2022) - [i27]Yuchen Cui, Scott Niekum, Abhinav Gupta, Vikash Kumar, Aravind Rajeswaran:
Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation? CoRR abs/2204.11134 (2022) - [i26]Gaoyue Zhou, Liyiming Ke, Siddhartha S. Srinivasa, Abhinav Gupta, Aravind Rajeswaran, Vikash Kumar:
Real World Offline Reinforcement Learning with Realistic Data Source. CoRR abs/2210.06479 (2022) - [i25]Nicklas Hansen, Yixin Lin, Hao Su, Xiaolong Wang, Vikash Kumar, Aravind Rajeswaran:
MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations. CoRR abs/2212.05698 (2022) - [i24]Zhao Mandi, Homanga Bharadhwaj, Vincent Moens, Shuran Song, Aravind Rajeswaran, Vikash Kumar:
CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learning. CoRR abs/2212.05711 (2022) - [i23]Nicklas Hansen, Zhecheng Yuan, Yanjie Ze, Tongzhou Mu, Aravind Rajeswaran, Hao Su, Huazhe Xu, Xiaolong Wang:
On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline. CoRR abs/2212.05749 (2022) - 2021
- [b1]Aravind Rajeswaran:
Broad Generalization through Domain Transfer: Abstractions and Algorithms. University of Washington, USA, 2021 - [c20]Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn:
Offline Reinforcement Learning from Images with Latent Space Models. L4DC 2021: 1154-1168 - [c19]Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn:
Visual Adversarial Imitation Learning using Variational Models. NeurIPS 2021: 3016-3028 - [c18]Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch:
Decision Transformer: Reinforcement Learning via Sequence Modeling. NeurIPS 2021: 15084-15097 - [c17]Wenling Shang, Xiaofei Wang, Aravind Srinivas, Aravind Rajeswaran, Yang Gao, Pieter Abbeel, Michael Laskin:
Reinforcement Learning with Latent Flow. NeurIPS 2021: 22171-22183 - [c16]Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn:
COMBO: Conservative Offline Model-Based Policy Optimization. NeurIPS 2021: 28954-28967 - [i22]Wenling Shang, Xiaofei Wang, Aravind Srinivas, Aravind Rajeswaran, Yang Gao, Pieter Abbeel, Michael Laskin:
Reinforcement Learning with Latent Flow. CoRR abs/2101.01857 (2021) - [i21]Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn:
COMBO: Conservative Offline Model-Based Policy Optimization. CoRR abs/2102.08363 (2021) - [i20]Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch:
Decision Transformer: Reinforcement Learning via Sequence Modeling. CoRR abs/2106.01345 (2021) - [i19]Catherine Cang, Aravind Rajeswaran, Pieter Abbeel, Michael Laskin:
Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL. CoRR abs/2106.09119 (2021) - [i18]Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn:
Visual Adversarial Imitation Learning using Variational Models. CoRR abs/2107.08829 (2021) - 2020
- [c15]Aravind Rajeswaran, Igor Mordatch, Vikash Kumar:
A Game Theoretic Framework for Model Based Reinforcement Learning. ICML 2020: 7953-7963 - [c14]Colin Summers, Kendall Lowrey, Aravind Rajeswaran, Siddhartha S. Srinivasa, Emanuel Todorov:
Lyceum: An efficient and scalable ecosystem for robot learning. L4DC 2020: 793-803 - [c13]Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims:
MOReL: Model-Based Offline Reinforcement Learning. NeurIPS 2020 - [i17]Colin Summers, Kendall Lowrey, Aravind Rajeswaran, Siddhartha S. Srinivasa, Emanuel Todorov:
Lyceum: An efficient and scalable ecosystem for robot learning. CoRR abs/2001.07343 (2020) - [i16]Aravind Rajeswaran, Igor Mordatch, Vikash Kumar:
A Game Theoretic Framework for Model Based Reinforcement Learning. CoRR abs/2004.07804 (2020) - [i15]Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims:
MOReL : Model-Based Offline Reinforcement Learning. CoRR abs/2005.05951 (2020) - [i14]Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn:
Offline Reinforcement Learning from Images with Latent Space Models. CoRR abs/2012.11547 (2020)
2010 – 2019
- 2019
- [c12]Kendall Lowrey, Aravind Rajeswaran, Sham M. Kakade, Emanuel Todorov, Igor Mordatch:
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control. ICLR (Poster) 2019 - [c11]Chelsea Finn, Aravind Rajeswaran, Sham M. Kakade, Sergey Levine:
Online Meta-Learning. ICML 2019: 1920-1930 - [c10]Divye Jain, Andrew Li, Shivam Singhal, Aravind Rajeswaran, Vikash Kumar, Emanuel Todorov:
Learning Deep Visuomotor Policies for Dexterous Hand Manipulation. ICRA 2019: 3636-3643 - [c9]Henry Zhu, Abhishek Gupta, Aravind Rajeswaran, Sergey Levine, Vikash Kumar:
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost. ICRA 2019: 3651-3657 - [c8]Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine:
Meta-Learning with Implicit Gradients. NeurIPS 2019: 113-124 - [i13]Chelsea Finn, Aravind Rajeswaran, Sham M. Kakade, Sergey Levine:
Online Meta-Learning. CoRR abs/1902.08438 (2019) - [i12]Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine:
Meta-Learning with Implicit Gradients. CoRR abs/1909.04630 (2019) - 2018
- [j2]Aravind Rajeswaran, Sridharakumar Narasimhan
, Shankar Narasimhan:
A graph partitioning algorithm for leak detection in water distribution networks. Comput. Chem. Eng. 108: 11-23 (2018) - [j1]Satya Jayadev P., Nirav P. Bhatt, Ramkrishna Pasumarthy, Aravind Rajeswaran:
Identifying Topology of Low Voltage Distribution Networks Based on Smart Meter Data. IEEE Trans. Smart Grid 9(5): 5113-5122 (2018) - [c7]Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine:
Divide-and-Conquer Reinforcement Learning. ICLR (Poster) 2018 - [c6]Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M. Bayen, Sham M. Kakade, Igor Mordatch, Pieter Abbeel:
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines. ICLR 2018 - [c5]Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, Sergey Levine:
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations. Robotics: Science and Systems 2018 - [c4]Kendall Lowrey, Svetoslav Kolev, Jeremy Dao, Aravind Rajeswaran, Emanuel Todorov:
Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system. SIMPAR 2018: 35-42 - [i11]Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M. Bayen, Sham M. Kakade, Igor Mordatch, Pieter Abbeel:
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines. CoRR abs/1803.07246 (2018) - [i10]Kendall Lowrey, Svetoslav Kolev, Jeremy Dao, Aravind Rajeswaran, Emanuel Todorov:
Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system. CoRR abs/1803.10371 (2018) - [i9]Henry Zhu, Abhishek Gupta, Aravind Rajeswaran, Sergey Levine, Vikash Kumar:
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost. CoRR abs/1810.06045 (2018) - [i8]Kendall Lowrey, Aravind Rajeswaran, Sham M. Kakade, Emanuel Todorov, Igor Mordatch:
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control. CoRR abs/1811.01848 (2018) - 2017
- [c3]Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran, Sergey Levine:
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles. ICLR (Poster) 2017 - [c2]Aravind Rajeswaran, Kendall Lowrey, Emanuel Todorov, Sham M. Kakade:
Towards Generalization and Simplicity in Continuous Control. NIPS 2017: 6550-6561 - [i7]Aravind Rajeswaran, Kendall Lowrey, Emanuel Todorov, Sham M. Kakade:
Towards Generalization and Simplicity in Continuous Control. CoRR abs/1703.02660 (2017) - [i6]Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, John Schulman, Emanuel Todorov, Sergey Levine:
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations. CoRR abs/1709.10087 (2017) - [i5]Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine:
Divide-and-Conquer Reinforcement Learning. CoRR abs/1711.09874 (2017) - 2016
- [c1]Satya Jayadev P., Aravind Rajeswaran, Nirav P. Bhatt, Ramkrishna Pasumarthy:
A novel approach for phase identification in smart grids using Graph Theory and Principal Component Analysis. ACC 2016: 5026-5031 - [i4]Aravind Rajeswaran, Sridharakumar Narasimhan, Shankar Narasimhan:
A Graph Partitioning Algorithm for Leak Detection in Water Distribution Networks. CoRR abs/1606.01754 (2016) - [i3]Satya Jayadev P., Nirav P. Bhatt, Ramkrishna Pasumarthy, Aravind Rajeswaran:
Identifying Topology of Power Distribution Networks Based on Smart Meter Data. CoRR abs/1609.02678 (2016) - [i2]Aravind Rajeswaran, Sarvjeet Ghotra, Sergey Levine, Balaraman Ravindran:
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles. CoRR abs/1610.01283 (2016) - 2015
- [i1]Aravind Rajeswaran, Shankar Narasimhan:
A New Method for Reconstructing Network Topology from Flux Measurements. CoRR abs/1506.00438 (2015)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
load content from web.archive.org
Privacy notice: By enabling the option above, your browser will contact the API of web.archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2023-03-16 21:58 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
