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Biography

Naman Shah

Post Doctoral Research Fellow

Brown University

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.

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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

Interests

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

Education

  • Ph.D.

    in Computer Science, -

    Arizona State University

  • M.S. in Computer Science, -

    Arizona State University

  • in Computer Engineering, -

    Gujarat Technological University

 

 

Applied Scientist Intern

Amazon Robotics

May – Aug 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 – Aug 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 – 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 – Dec Arizona

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

Responsibilites include:

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

Naman Shah, Siddharth Srivastava

May AAMAS 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 PlanRob 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