Anurag Ajay

I am a Ph.D. student in EECS at MIT CSAIL advised by Professor Pulkit Agrawal. I am grateful for support from MIT presidential fellowship.

In May 2019, I received my S.M. in EECS from MIT advised by Professor Leslie Kaelbling and Professor Josh Tenenbaum. Before MIT, I completed my bachelor's degree in EECS from UC Berkeley where I worked with Professor Pieter Abbeel and Professor Sergey Levine.

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Research

My research goal is to design algorithms that can enable agents to continuously interact, learn and perform complex tasks in their environments. Currently, I am working on designing deep reinforcement and imitation learning methods that can endow robots with diverse, transferable and composable skills.

In the past, I have worked on using deep learning for state estimation, building model-based deep reinforcement learning algorithms for robotics and learning dynamics model, with the help of physics engines, in the robotics domain.

PontTuset Long-Horizon Prediction and Uncertainty Propagation with Residual Point Contact Learners
Nima Fazeli, Anurag Ajay, Alberto Rodriguez
ICRA, 2020   (In submission)

PontTuset Learning skill hierarchies from predicate descriptions and self-supervision
Tom Silver*, Rohan Chitnis*, Anurag Ajay, Leslie Kaelbling, Josh Tenenbaum
AAAI GenPlan Workshop, 2020

We learn lifted, goal-conditioned policies and use STRIPS planning with learned operator descriptions to solve a large suite of unseen test tasks.

PontTuset Learning to Navigate Endoscopic Capsule Robots
Mehmet Turan, Yasin Almalioglu, Hunter Gilbert, Faisal Mahmood, Nicholas Durr, Helder Araujo, Alp Eren Sarı, Anurag Ajay, Metin Sitti
IEEE RAL, 2019
paper / bibtex

We use deep reinforcement learning algorithms for controlling endoscopic capsule robots

PontTuset Combining Physical Simulators and Object-Based Networks for Control
Anurag Ajay, Maria Bauza, Jiajun Wu, Nima Fazeli, Josh Tenenbaum, Alberto Rodriguez, Leslie Kaelbling
ICRA, 2019
arXiv / bibtex / project page

We propose a hybrid dynamics model, simulator-augmented interaction networks (SAIN), combining a physics engine with an object-based neural network for dynamics modeling. Compared with existing models that are purely analytical or purely data-driven, our hybrid model captures the dynamics of interacting objects in a more accurate and data-efficient manner.

PontTuset Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing
Anurag Ajay, Jiajun Wu, Nima Fazeli, Maria Bauza, Leslie Kaelbling, Josh Tenenbaum, Alberto Rodriguez
IROS, 2018   (Best Paper on Cognitive Robotics)
arXiv / bibtex / project page

Combining symbolic, deterministic simulators with learnable, stochastic neural nets provides us with expressiveness, efficiency, and generalizability simultaneously. Our model outperforms both purely analytical and purely learned simulators consistently on real, standard benchmarks.

PontTuset Reset-Free Guided Policy Search: Efficient Deep Reinforcement Learning with Stochastic Initial States
William Montgomery*, Anurag Ajay*, Chelsea Finn, Pieter Abbeel, Sergey Levine
ICRA, 2017
arXiv / bibtex / project page

We present a new guided policy search algorithm that allows the method to be used in domains where the initial conditions are stochastic, which makes the method more applicable to general reinforcement learning problems and improves generalization performance in our robotic manipulation experiments.

PontTuset Backprop KF: Learning Discriminative Deterministic State Estimators
Tuomas Haarnoja, Anurag Ajay, Sergey Levine, Pieter Abbeel
NIPS, 2016
arXiv / bibtex

We represent kalman filter as a computational graph and use it in place of recurrent neural networks for data-efficient end to end visual state estimation.

Service
Reviewer for IROS, IEEE-RAL, ICRA, ICML, NeurIPS
uGSI for CS 189 (Introduction to Machine Learning) Fall 2016, Spring 2017

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