I am a PhD candidate in computer science (robotics) at the University of Southern California (USC), advised by Prof. Gaurav Sukhatme. Broadly, I work on combining planning algorithms with learning to achieve greater autonomy for robots, with experience in object manipulation, control theory, and deep reinforcement learning.
Download my résumé (2 page) and CV (full).
- Robot manipulation
- Deformable Objects
- Learning & Optimization
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MSc in Computer Science (Robotics & Perception), 2018
Georgia Tech
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MSc in Mechanical Engineering (Control Theory), 2012
University of Texas at Austin
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Mechanical Engineering, 2010
Indian Institute of Technology Bombay (IIT Bombay)
News
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Sep 2022: Two papers will be presented at the Southern California Robotics Symposium 2022.
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Jun - Sep 2022: I will be an Applied Scientist intern at Amazon Robotics.
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Jun 2022: Our paper on learning deformable object manipulation from expert demonstrations has been accepted to both IEEE RA-L and IEEE IROS 2022. See you in Kyoto! (Website || RA-L link)
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May 2022: I presented our work on learning deformable object manipulation from expert demonstrations at the 2nd workshop on deformable object manipulation at ICRA 2022 in Philadelphia. (Website || PDF || Video)
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Mar 2021: Our paper on adaptive sampling with POMDPs for environmental monitoring has been accepted to ICRA 2021! (PDF)
Work Experience
- Reinforcement Learning for peg insertion tasks (environments, learning and classical control methods)
- Developed ROS package to deploy a learned algorithm, tested on robot hardware.
- Implemented object manipulation algorithms to pick & place cases in warehouse storage and retrieval systems (C++, python).
- Worked on low-level controllers for actuator performance and stall detection.
Selected Publications
See Google Scholar for a full list
Service
Review
- Conferences: ISER, ICRA
- Journals: AURO, IEEE RA-L
- Other: US Department of Energy (office of SBIR/STTR), robotics workshops
Chair
- Chaired ICRA 2021 session on ‘Field Robotics: Control’