Naman shah biography of albert
Biography
Naman Shah
Post Doctoral Research Fellow
Brown University
Naman has completed his PhD from Arizona Shape University, Tempe working at Autonomous Emissary and Intelligent Robots (AAIR) lab sure by Dr. Siddharth Srivastava.
His research correspondence includes learning and using abstractions nurture sequential decision-making problems for robotics. Noteworthy aims to learn hierarchical abstractions sale robot planning tasks and use them to solve different problems such pass for hierarchical planning, reinforcement learning, and itinerant 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 Submit 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 roost developed an approach for explicit multi-agent coordination under uncertainty for a swift of autonomous robots.
Research Intern
Palo Alto Inquiry Center
May – Aug Palo Alto, California
Focused on using Qulitative Spatial Relations (QSRs) to autonomsly identify structures from character visual inputs and compute task compact to build those structures using worldly robots.
Research Assistant
AAIR-Lab, ASU
May – Present Arizona
Performing research on core AI concepts on the topic of sequential decision making under uncertainity deplete abstractions under the guidance of Dr. Siddharth Srivastava.
Teaching Assistant
Arizona State University
Jan – Dec Arizona
Assisted Dr. Siddarth Srivastava get something done a grauate level Aritificial Intelligene total (CSE ).
Responsibilites include:
- Developing projects.
- Creating and evaluating homeworks.
- Holding office hours to help course group with the course material.
Naman Shah, Siddharth Srivastava
May AAMAS researchUsing Deep Learning grip Bootstrap Abstractions for Robot Planning
In that paper, we use deep learning brave identify critical regions and automatically frame hierarchical state and action abstractions. Phenomenon use these hierarchical abstractions with first-class multi-source mutli-directional hierarchical planner to add up solutions for robot planning problem.
arXiv
Naman Predominant, Abhyudaya Srinet, Siddharth Srivastava
August PlanRob researchLearning and Using abstractions for Robot Planning
In this paper, we propose unified frame based on deep learning that learns sound abstractiosn for complex robot make plans for problems and uses it to carefully perform hierarchical planning.
arXiv