Papers
32
Total Citations
534
H-Index
10
About
Ahmed H. Qureshi is a pioneering robotics researcher whose work spans two transformative domains: human-robot interaction and intelligent motion planning. His research addresses some of the most pressing challenges in modern robotics — enabling machines to navigate complex environments efficiently while interacting naturally and safely alongside humans. Qureshi's early contributions focused on teaching robots socially intelligent behaviors through deep reinforcement learning. His Multimodal Deep Q-Network framework (111 citations) demonstrated that robots could acquire human-like social skills through multimodal sensory learning, a breakthrough later refined through neural attention mechanisms and intrinsically motivated reinforcement learning approaches. These works collectively established a foundation for deploying socially aware robots in real-world settings. Equally significant is his prolific output in learning-based motion planning. From his Deeply Informed Neural Sampling work (88 citations) to Motion Planning Networks and the MPC-MPNet framework (57 citations), Qureshi has consistently pushed toward faster, near-optimal planning solutions under complex kinodynamic constraints — work that directly benefits autonomous vehicles, surgical robotics, and industrial manipulators. His more recent research incorporating Transformers and neural Lyapunov control signals continued innovation toward scalable, safe robot autonomy. With hundreds of citations accumulated across a relatively compact body of work, Qureshi has established himself as a significant voice shaping the future of intelligent, human-centered robotics.
Research Focus
Key Achievements
Top Papers
- 1Robot gains social intelligence through multimodal deep reinforcement learning111 citations · 2016
- 2Deeply Informed Neural Sampling for Robot Motion Planning88 citations · 2018
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- 7Robot Active Neural Sensing and Planning in Unknown Cluttered Environments16 citations · 2023
- 8Motion Planning Networks15 citations · 2019
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- 10Model-free Neural Lyapunov Control for Safe Robot Navigation10 citations · 2022