I am a PhD candidate 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
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 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.
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.