Ahmed Qureshi
Assistant Professor of Computer Science
Our team is dedicated to advancing the field of robot planning and control through a combination of fundamental and applied research. Our primary objective is to develop robots that can operate effectively and safely in natural and dynamic human environments. To achieve this, we focus on a range of challenges, such as scalable and fast motion planning, active perception, human-in-the-loop robot manipulation, mobile navigation, and dynamic control. The ulti… ↓More
Joined department: Fall 2021
Research Areas
- Robotics and Computer Vision
- Artificial Intelligence, Machine Learning, and Natural Language Processing
Education
Ph.D., University of California San Diego, Intelligent Systems, Robotics and Control (2021)
M.S., Osaka University, Robotics (2017)
B.S., National University of Science and Technology, Electrical Engineering (2014)
Our team is dedicated to advancing the field of robot planning and control through a combination of fundamental and applied research. Our primary objective is to develop robots that can operate effectively and safely in natural and dynamic human environments. To achieve this, we focus on a range of challenges, such as scalable and fast motion planning, active perception, human-in-the-loop robot manipulation, mobile navigation, and dynamic control. The ultimate goal of our research is to enhance people’s lives and optimize workforce efficiency in various economic and healthcare sectors.
Selected Publications
Ruiqi Ni and Ahmed H. Qureshi, NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning, International Conference on Representation Learning (ICLR), 2023. [SPOTLIGHT]
Ruiqi Ni and Ahmed H. Qureshi, Progressive Learning for Physics-informed Neural Motion Planning, Robotics: Science and Systems (RSS), 2023.
Manav Kulshrestha and Ahmed H. Qureshi, Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning, Conference on Robot Learning (CoRL), 2023.
Vivek Gupta, Praphpreet Dhir, Jeegn Dani, and Ahmed H. Qureshi, MANER: Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environments, IEEE Robotics and Automation Letters, 2023.
Zixing Wang and Ahmed H. Qureshi, DeRi-Bot: Learning to Collaboratively Manipulate Rigid Objects via Deformable Objects, IEEE Robotics and Automation Letters, 2023.