Aravind Rajeswaran

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

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

For summer 2019, I am interning at Google Brain Robotics with Vikash Kumar and Igor Mordatch. I was at NVidia Robotics for summer 2018, interning with Dieter Fox and Byron Boots. In summer 2017, I interned at OpenAI with 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.


News
  • Started summer internship at Google Brain! (June 2019)
  • Completed PhD qualifying exam! (April 2019)
  • Organizing the workshop on generative modeling and model-based RL at ICML 2019.
  • Online Meta-Learning paper accepted at ICML 2019. Bringing meta-learning and online/lifelong learning closer.
  • POLO paper is accepted at ICLR 2019. Solve dexterous in-hand manipulation in < 20 mins on your laptop!
  • Two papers accepted at International Conference on Robotics and Automation (ICRA) 2019.

Representative Papers

Meta Learning with Implicit Gradients
Aravind Rajeswaran, Chelsea Finn, Sham Kakade, Sergey Levine
Neural Information Processing Systems (NeurIPS) 2019; arXiv:1909.04630

Online Meta-Learning
Chelsea Finn, Aravind Rajeswaran, Sham Kakade, Sergey Levine
International Conference on Machine Learning (ICML) 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; Project Website

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; Project Website

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


All Publications and Preprints

Meta Learning with Implicit Gradients
Aravind Rajeswaran, Chelsea Finn, Sham Kakade, Sergey Levine
Neural Information Processing Systems (NeurIPS) 2019; arXiv:1909.04630

Online Meta-Learning
Chelsea Finn, Aravind Rajeswaran, Sham Kakade, Sergey Levine
International Conference on Machine Learning (ICML) 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; download PDF

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)


Mentoring

I enjoy collaborating with a diverse set of researchers, and have also had the pleasure of mentoring highly motivated undergraduate students. Some of them are:

  • Ben Evans, Colin Summers, and Brian Chan : model-based RL (w/ Kendall Lowrey)
  • Divye Jain : visual imitation learning