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. 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@asu.edu
Ph.D. in Computer Science, 2019 - 2024
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:
Traditional robot planning relies on human-crafted logic representations, but this paper introduces a method to autonomously learn abstract representations from raw robot data. Results show these learned models enable scalable planning for complex tasks without human intervention.
This paper proposes a new method for robots to plan actions in complex environments, even when the environment is unknown. The robot learns to create its own high-level actions without needing pre-programmed ones. This allows the robot to quickly solve new problems in unseen environments. The method is shown to be faster and achieve significantly better solutions than existing approaches
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.