Naman has completed his PhD from Arizona State University, Tempe working at Autonomous Agent and Intelligent Robots (AAIR) lab directed by Dr. Siddharth Srivastava.
His research interest includes learning and using abstractions for sequential decision-making problems for robotics. He aims to learn hierarchical abstractions for robot planning tasks and use them to solve different problems such as hierarchical planning, reinforcement learning, and mobile manipulation in stochastic settings.
Email: namanshah@asu.edu
Ph.D. in Computer Science, 2019 - 2024
Arizona State University
M.S. in Computer Science, 2017 - 2019
Arizona State University
B.Eng. in Computer Engineering, 2013 - 2017
Gujarat Technological University
May 2022 – Aug 2022 North Reading, Massachusetts
Designed and developed an approach for explicit multi-agent coordination under uncertainty for a fleet of autonomous robots.
May 2019 – Aug 2019 Palo Alto, California
Focused on using Qulitative Spatial Relations (QSRs) to autonomsly identify structures from the visual inputs and compute task plans to build those structures using physical robots.
May 2018 – Present Arizona
Performing research on core AI concepts like sequential decision making under uncertainity using abstractions under the guidance of Dr. Siddharth Srivastava.
Jan 2016 – Dec 2016 Arizona
Assisted Dr. Siddarth Srivastava for a grauate level Aritificial Intelligene course (CSE 571).
Responsibilites include:
Naman Shah, Siddharth Srivastava
May 2022AAMAS 2022researchIn this paper, we use deep learning to identify critical regions and automatically construct hierarchical state and action abstractions. We use these hierarchical abstractions with a multi-source mutli-directional hierarchical planner to compute solutions for robot planning problem.
arXiv
Naman Shah, Abhyudaya Srinet, Siddharth Srivastava
August 2021PlanRob 2021researchIn this paper, we propose unified framework based on deep learning that learns sound abstractiosn for complex robot planning problems and uses it to efficiently perform hierarchical planning.
arXiv