I am a 2nd 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
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
Assisted Dr. Siddarth Srivastava for a grauate level Aritificial Intelligene course (CSE 571).
Responsibilites include:
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.
In this paper, we provide and efficient abstraction based methods to compute task and motion policies for complex robotics task for stochastic environments.
The talk was given at PlanRob 2021. It talks about the framework we developed to learn and use abstractions hierarchies for efficient robot planning.
In this talk, I have presented my paper of abstraction and hierarchical refinement based combined task and motion planning approach at ICRA 2020.