A New Method for Reconstructing Network Topology from Flux Measurements. - Aravind Rajeswaran; Shankar Narasimhan (CoRR, 2015)

A novel approach for phase identification in smart grids using Graph Theory and Principal Component Analysis. - Satya Jayadev P.; Aravind Rajeswaran; Nirav P. Bhatt; Ramkrishna Pasumarthy (ACC, 2016)

Identifying Topology of Power Distribution Networks Based on Smart Meter Data. - Satya Jayadev P.; Nirav P. Bhatt; Ramkrishna Pasumarthy; Aravind Rajeswaran (CoRR, 2016)

EPOpt: Learning Robust Neural Network Policies Using Model Ensembles. - Aravind Rajeswaran; Sarvjeet Ghotra; Balaraman Ravindran; Sergey Levine (ICLR (Poster), 2017)

Towards Generalization and Simplicity in Continuous Control. - Aravind Rajeswaran; Kendall Lowrey; Emanuel Todorov; Sham M. Kakade (NIPS, 2017)

Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations. - Aravind Rajeswaran; Vikash Kumar; Abhishek Gupta 004; John Schulman; Emanuel Todorov; Sergey Levine (CoRR, 2017)

Divide-and-Conquer Reinforcement Learning. - Dibya Ghosh; Avi Singh; Aravind Rajeswaran; Vikash Kumar; Sergey Levine (ICLR (Poster), 2018)

Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines. - Cathy Wu 002; Aravind Rajeswaran; Yan Duan; Vikash Kumar; Alexandre M. Bayen; Sham M. Kakade; Igor Mordatch; Pieter Abbeel (ICLR, 2018)

Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations. - Aravind Rajeswaran; Vikash Kumar; Abhishek Gupta 004; Giulia Vezzani; John Schulman; Emanuel Todorov; Sergey Levine (Robotics: Science and Systems, 2018)

Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system. - Kendall Lowrey; Svetoslav Kolev; Jeremy Dao; Aravind Rajeswaran; Emanuel Todorov (SIMPAR, 2018)

A graph partitioning algorithm for leak detection in water distribution networks. - Aravind Rajeswaran; Sridharakumar Narasimhan; Shankar Narasimhan (Comput. Chem. Eng., 2018)

Identifying Topology of Low Voltage Distribution Networks Based on Smart Meter Data. - Satya Jayadev P.; Nirav P. Bhatt; Ramkrishna Pasumarthy; Aravind Rajeswaran (IEEE Trans. Smart Grid, 2018)

Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control. - Kendall Lowrey; Aravind Rajeswaran; Sham M. Kakade; Emanuel Todorov; Igor Mordatch (ICLR (Poster), 2019)

Online Meta-Learning. - Chelsea Finn; Aravind Rajeswaran; Sham M. Kakade; Sergey Levine (ICML, 2019)

Learning Deep Visuomotor Policies for Dexterous Hand Manipulation. - Divye Jain; Andrew Li; Shivam Singhal; Aravind Rajeswaran; Vikash Kumar; Emanuel Todorov (ICRA, 2019)

Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost. - Henry Zhu; Abhishek Gupta 004; Aravind Rajeswaran; Sergey Levine; Vikash Kumar (ICRA, 2019)

Meta-Learning with Implicit Gradients. - Aravind Rajeswaran; Chelsea Finn; Sham M. Kakade; Sergey Levine (NeurIPS, 2019)

A Game Theoretic Framework for Model Based Reinforcement Learning. - Aravind Rajeswaran; Igor Mordatch; Vikash Kumar (ICML, 2020)

Lyceum: An efficient and scalable ecosystem for robot learning. - Colin Summers; Kendall Lowrey; Aravind Rajeswaran; Siddhartha S. Srinivasa; Emanuel Todorov (L4DC, 2020)

MOReL: Model-Based Offline Reinforcement Learning. - Rahul Kidambi; Aravind Rajeswaran; Praneeth Netrapalli; Thorsten Joachims (NeurIPS, 2020)

Offline Reinforcement Learning from Images with Latent Space Models. - Rafael Rafailov; Tianhe Yu; Aravind Rajeswaran; Chelsea Finn (L4DC, 2021)

Decision Transformer: Reinforcement Learning via Sequence Modeling. - Lili Chen; Kevin Lu; Aravind Rajeswaran; Kimin Lee; Aditya Grover; Michael Laskin; Pieter Abbeel; Aravind Srinivas; Igor Mordatch (NeurIPS, 2021)

Visual Adversarial Imitation Learning using Variational Models. - Rafael Rafailov; Tianhe Yu; Aravind Rajeswaran; Chelsea Finn (NeurIPS, 2021)

Reinforcement Learning with Latent Flow. - Wenling Shang; Xiaofei Wang 008; Aravind Srinivas; Aravind Rajeswaran; Yang Gao 029; Pieter Abbeel; Michael Laskin (NeurIPS, 2021)

COMBO: Conservative Offline Model-Based Policy Optimization. - Tianhe Yu; Aviral Kumar; Rafael Rafailov; Aravind Rajeswaran; Sergey Levine; Chelsea Finn (NeurIPS, 2021)

Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL. - Catherine Cang; Aravind Rajeswaran; Pieter Abbeel; Michael Laskin (CoRR, 2021)

Broad Generalization through Domain Transfer: Abstractions and Algorithms. - Aravind Rajeswaran (2021)

R3M: A Universal Visual Representation for Robot Manipulation. - Suraj Nair 003; Aravind Rajeswaran; Vikash Kumar; Chelsea Finn; Abhinav Gupta 001 (CoRL, 2022)

The Unsurprising Effectiveness of Pre-Trained Vision Models for Control. - Simone Parisi; Aravind Rajeswaran; Senthil Purushwalkam; Abhinav Gupta 001 (ICML, 2022)

Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots. - Tanmay Shankar; Yixin Lin; Aravind Rajeswaran; Vikash Kumar; Stuart Anderson; Jean Oh (ICML, 2022)

Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation? - Yuchen Cui; Scott Niekum; Abhinav Gupta 001; Vikash Kumar; Aravind Rajeswaran (L4DC, 2022)

Unsupervised Reinforcement Learning with Contrastive Intrinsic Control. - Michael Laskin; Hao Liu 055; Xue Bin Peng; Denis Yarats; Aravind Rajeswaran; Pieter Abbeel (NeurIPS, 2022)

CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery. - Michael Laskin; Hao Liu 055; Xue Bin Peng; Denis Yarats; Aravind Rajeswaran; Pieter Abbeel (CoRR, 2022)

CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learning. - Zhao Mandi; Homanga Bharadhwaj; Vincent Moens; Shuran Song; Aravind Rajeswaran; Vikash Kumar (CoRR, 2022)

MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations. - Nicklas Hansen 001; Yixin Lin; Hao Su 001; Xiaolong Wang 004; Vikash Kumar; Aravind Rajeswaran (ICLR, 2023)

On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline. - Nicklas Hansen 001; Zhecheng Yuan; Yanjie Ze; Tongzhou Mu; Aravind Rajeswaran; Hao Su 001; Huazhe Xu; Xiaolong Wang 004 (ICML, 2023)

Masked Trajectory Models for Prediction, Representation, and Control. - Philipp Wu; Arjun Majumdar; Kevin Stone; Yixin Lin; Igor Mordatch; Pieter Abbeel; Aravind Rajeswaran (ICML, 2023)

Train Offline, Test Online: A Real Robot Learning Benchmark. - Gaoyue Zhou; Victoria Dean; Mohan Kumar Srirama; Aravind Rajeswaran; Jyothish Pari; Kyle Hatch; Aryan Jain; Tianhe Yu; Pieter Abbeel; Lerrel Pinto; Chelsea Finn; Abhinav Gupta 001 (ICRA, 2023)

Real World Offline Reinforcement Learning with Realistic Data Source. - Gaoyue Zhou; Liyiming Ke; Siddhartha S. Srinivasa; Abhinav Gupta 001; Aravind Rajeswaran; Vikash Kumar (ICRA, 2023)

RoboHive: A Unified Framework for Robot Learning. - Vikash Kumar; Rutav M. Shah; Gaoyue Zhou; Vincent Moens; Vittorio Caggiano; Abhishek Gupta 004; Aravind Rajeswaran (NeurIPS, 2023)

Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence? - Arjun Majumdar; Karmesh Yadav; Sergio Arnaud; Yecheng Jason Ma; Claire Chen; Sneha Silwal; Aryan Jain; Vincent-Pierre Berges; Tingfan Wu; Jay Vakil; Pieter Abbeel; Jitendra Malik; et al. (NeurIPS, 2023)

Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence? - Arjun Majumdar; Karmesh Yadav; Sergio Arnaud; Yecheng Jason Ma; Claire Chen; Sneha Silwal; Aryan Jain; Vincent-Pierre Berges; Pieter Abbeel; Jitendra Malik; Dhruv Batra; Yixin Lin; et al. (CoRR, 2023)

RoboHive: A Unified Framework for Robot Learning. - Vikash Kumar; Rutav M. Shah; Gaoyue Zhou; Vincent Moens; Vittorio Caggiano; Jay Vakil; Abhishek Gupta 004; Aravind Rajeswaran (CoRR, 2023)

OpenEQA: Embodied Question Answering in the Era of Foundation Models. - Arjun Majumdar; Anurag Ajay; Xiaohan Zhang 002; Pranav Putta; Sriram Yenamandra; Mikael Henaff; Sneha Silwal; Paul McVay; Oleksandr Maksymets; Sergio Arnaud; Karmesh Yadav; Qiyang Li; et al. (CVPR, 2024)

MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation. - Patrick Lancaster; Nicklas Hansen 001; Aravind Rajeswaran; Vikash Kumar (ICRA, 2024)

What Do We Learn from a Large-Scale Study of Pre-Trained Visual Representations in Sim and Real Environments? - Sneha Silwal; Karmesh Yadav; Tingfan Wu; Jay Vakil; Arjun Majumdar; Sergio Arnaud; Claire Chen; Vincent-Pierre Berges; Dhruv Batra; Aravind Rajeswaran; Mrinal Kalakrishnan; Franziska Meier; et al. (ICRA, 2024)

From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot Control. - Yide Shentu; Philipp Wu; Aravind Rajeswaran; Pieter Abbeel (IROS, 2024)

Semi-Supervised One Shot Imitation Learning. - Philipp Wu; Kourosh Hakhamaneshi; Yuqing Du; Igor Mordatch; Aravind Rajeswaran; Pieter Abbeel (RLJ, 2024)

Policy Architectures for Compositional Generalization in Control. - Allan Zhou; Vikash Kumar; Chelsea Finn; Aravind Rajeswaran (RLJ, 2024)

LIFT-GS: Cross-Scene Render-Supervised Distillation for 3D Language Grounding. - Ang Cao; Sergio Arnaud; Oleksandr Maksymets; Jianing Yang; Ayush Jain; Sriram Yenamandra; Ada Martin; Vincent-Pierre Berges; Paul McVay; Ruslan Partsey; Aravind Rajeswaran; Franziska Meier; et al. (CoRR, 2025)

Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D. - Sergio Arnaud; Paul McVay; Ada Martin; Arjun Majumdar; Krishna Murthy Jatavallabhula; Phillip Thomas; Ruslan Partsey; Daniel Dugas; Abha Gejji; Alexander Sax; Vincent-Pierre Berges; Mikael Henaff; et al. (CoRR, 2025)