Naman Shah

PhD Student

Arizona State Univeristy

Biography

I am a 2^nd year Ph.D. student working in Autonomous Agent and Intelligent Robots (AAIR) lab under the guidance of Dr. Siddharth Srivastava at Arizona State University, Tempe, USA.

My research interest lies in using abstraction to efficiently perform hierarchical planning to solve complex robotics tasks under uncertainty. I use concepts of hierarchical abstractions to solve different problems such as hierarchical planning and mobile manipulation in stochastic settings.

Email: namanshah@asu.edu

Interests

Artificial Intelligence
Robotics
Reinforcement Learning
Machine Learning

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

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, 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

Recent & Upcoming Talks

Aug 5, 2021 9:00 AM Virtual (Originally Guangzhou, China)

Learning and Using Abstractions for Robot Planning

The talk was given at PlanRob 2021. It talks about the framework we developed to learn and use abstractions hierarchies for efficient robot planning.

Video Paper

May 31, 2020 9:00 AM Virtual (Originally Paris, France)

Anytime Task and Motion Policies for Stochastic Environments

In this talk, I have presented my paper of abstraction and hierarchical refinement based combined task and motion planning approach at ICRA 2020.