Aravind Rajeswaran

PhD student in machine learning + robotics,
Computer Science & Engineering,
University of Washington

Contact: aravraj at cs.washington.edu
google scholar | github | twitter


I am a third year PhD student at UW with Sham Kakade and Emo Todorov. I also collaborate with Sergey Levine and his students. I am interested in the mathematical foundations and applications of machine learning. My current research leverages learning and optimization to endow robots with a vast repertoire of skills. To this end, I draw upon deep reinforcement learning, model-based control, and meta-learning.

I interned at Nvidia in Summer 2018 with Dieter Fox and Byron Boots. In summer 2017, I interned at OpenAI with Pieter Abbeel, John Schulman, and Igor Mordatch. Previously, I was an undergraduate student at IIT Madras, where I worked with Balaraman Ravindran. Before switching focus to AI, I worked on statistical physics of complex networks, and recieved the best undergraduate thesis award from IIT Madras.


Selected Publications

Towards Generalization and Simplicity in Continuous Control
Aravind Rajeswaran, Kendall Lowrey, Emanuel Todorov, Sham Kakade
Neural Information Processing Systems (NIPS) 2017; arXiv:1703.02660

Online Meta-Learning
Chelsea Finn, Aravind Rajeswaran, Sham Kakade, Sergey Levine
Under review. arXiv preprint (2019); arXiv:1902.08438

EPOpt: Learning Robust Neural Network Policies Using Model Ensembles
Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran, Sergey Levine
International Conference on Learning Representations (ICLR) 2017; arXiv:1610.01283

Learning Complex Dexterous Manipulation with Deep Reinforcement
Learning and Demonstrations

Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, John Schulman,
Emanuel Todorov, Sergey Levine
Robotics: Science and Systems (RSS) 2018; arxiv:1709.10087

Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control
Kendall Lowrey, Aravind Rajeswaran, Sham Kakade, Emanuel Todorov, Igor Mordatch
International Conference on Learning Representations (ICLR) 2019; arXiv:1811.01848

Reinforcement learning for non-prehensile manipulation: Transfer
from simulation to physical system

Kendall Lowrey, Svetsolav Kolev, Jeremy Dao, Aravind Rajeswaran, Emanuel Todorov
IEEE SIMPAR 2018; arXiv:1803.10371 (Best paper award!)


All Publications and Preprints

Online Meta-Learning
Chelsea Finn, Aravind Rajeswaran, Sham Kakade, Sergey Levine
Under review. arXiv preprint (2019); arXiv:1902.08438

Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control
Kendall Lowrey, Aravind Rajeswaran, Sham Kakade, Emanuel Todorov, Igor Mordatch
International Conference on Learning Representations (ICLR) 2019; arXiv:1811.01848

Dexterous Manipulation with Deep Reinforcement Learning: Efficient,
General, and Low Cost

Henry Zhu, Abhishek Gupta, Aravind Rajeswaran, Sergey Levine, Vikash Kumar
International Conference on Robotics and Automation (ICRA) 2019; arXiv:1810.06045

Learning Deep Visuomotor Policies for Dexterous Hand Manipulation
Divye Jain, Andrew Li, Shivam Singhal, Aravind Rajeswaran, Vikash Kumar, Emanuel Todorov
International Conference on Robotics and Automation (ICRA) 2019; (pdf coming soon)

Reinforcement learning for non-prehensile manipulation: Transfer
from simulation to physical system

Kendall Lowrey, Svetsolav Kolev, Jeremy Dao, Aravind Rajeswaran, Emanuel Todorov
IEEE SIMPAR 2018; arXiv:1803.10371 (Best paper award!)

Learning Complex Dexterous Manipulation with Deep Reinforcement
Learning and Demonstrations

Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, John Schulman,
Emanuel Todorov, Sergey Levine
Robotics: Science and Systems (RSS) 2018; arxiv:1709.10087

Variance Reduction for Policy Gradient Using Action-Dependent Factorized Baselines
Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M Bayen,
Sham Kakade, Igor Mordatch, Pieter Abbeel
International Conference on Learning Representations (ICLR) 2018; arXiv:1803.07246

Divide-and-Conquer Reinforcement Learning
Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine
International Conference on Learning Representations (ICLR) 2018; arXiv:1711.09874

Towards Generalization and Simplicity in Continuous Control
Aravind Rajeswaran, Kendall Lowrey, Emanuel Todorov, Sham Kakade
Neural Information Processing Systems (NIPS) 2017; arXiv:1703.02660

EPOpt: Learning Robust Neural Network Policies Using Model Ensembles
Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran, Sergey Levine
International Conference on Learning Representations (ICLR) 2017; arXiv:1610.01283

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
American Control Conference (ACC) 2016; arXiv:1511.06063

Identifying Topology of Power Distribution Networks Based on Smart Meter Data
Jayadev P Satya, Nirav Bhatt, Ramkrishna Pasumarthy, Aravind Rajeswaran
IEEE Transactions on Smart Grid 2017; arXiv:1609.02678

A Graph Partitioning Approach for Leak Detection in Water Distribution Networks
Aravind Rajeswaran, Sridharakumar Narasimhan, Shankar Narasimhan
Computers & Chemical Engineering (C&ChE) 2017; arXiv:1606.01754


Teaching

CSE599G: Deep Reinforcement Learning (Instructor)
I co-taught a course on deep reinforcement learning at UW in Spring 2018. The course takes a broad perspective on RL and covers topics including tabular dynamic programming, actor critic algorithms, trajectory optimization, MCTS, and guided policy search. I co-designed the course structure and all the teaching material. This course is inspired by the offerings of Emo Todorov, Balaraman Ravindran, and Sergey Levine.

CSE547: Machine Learning for Big Data (Teaching Assistant)