Naman Shah

PhD Student

Arizona State Univeristy

Biography

I am a 4^th year PhD student working in Autonomous Agent and Intelligent Robots (AAIR) lab directed by Dr. Siddharth Srivastava at Arizona State University, Tempe, USA.

My research interest includes learning and using abstractions for sequential decision-making problems for robotics. I aim 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

Interests

Artificial Intelligence
Robotics
Learning Abstractions
Task and Motion Planning
Reinforcement Learning
Hierarchical Planning

Education

Ph.D. in Computer Science, 2019 - Present
Arizona State University
M.S. in Computer Science, 2017 - 2019
Arizona State University
B.Eng. in Computer Engineering, 2013 - 2017
Gujarat Technological University

Experience

Applied Scientist Intern

Amazon Robotics

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.

Research Intern

Palo Alto Research Center

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.

Research Assistant

AAIR-Lab, ASU

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.

Teaching Assistant

Arizona State University

Jan 2016 – Dec 2016 Arizona

Assisted Dr. Siddarth Srivastava for a grauate level Aritificial Intelligene course (CSE 571).

Responsibilites include:

Developing projects.
Creating and evaluating homeworks.
Holding office hours to help students with the course material.

Publications

Naman Shah, Siddharth Srivastava

May 2022 AAMAS 2022 research

Using Deep Learning to Bootstrap Abstractions for Robot Planning

In 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 2021 PlanRob 2021 research

Learning and Using abstractions for Robot Planning

In 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

Naman Shah, Deepak KalaVasudevan, Kislay Kumar, Pranav Kamojhalla, Siddharth Srivastava

May 2020 ICRA 2020 research

Anytime Task and Motion Policies for Stochastic Envrionments

In this paper, we provide and efficient abstraction based methods to compute task and motion policies for complex robotics task for stochastic environments.

arXiv Project Webpage Source Code Videos Presentation