Papers
19
Total Citations
462
H-Index
11
About
Ryan Hoque is a robotics researcher whose work sits at the intersection of robot learning, deformable object manipulation, and human-robot interaction. He is perhaps best known for his sustained focus on robotic fabric manipulation — a notoriously difficult problem due to the complex, high-dimensional dynamics of deformable materials. His early landmark paper on deep imitation learning for sequential fabric smoothing (2020, 109 citations) demonstrated that robots could learn effective pulling policies from an algorithmic supervisor using RGB-D perception, while his VisuoSpatial Foresight series (71 citations) extended visual planning frameworks to enable generalization across multiple fabric tasks. His 2021 work on dense visual correspondences bridged simulation and the real world, advancing sim-to-real transfer for garment manipulation. Beyond fabric manipulation, Hoque has made meaningful contributions to interactive imitation learning, developing algorithms like LazyDAgger and ThriftyDAgger that intelligently manage when and how human supervisors intervene during robot training — reducing operator burden without sacrificing learning quality. His research on cloud robotics platforms, including FogROS2-SGC, reflects a broader interest in making robot learning scalable and accessible. Collectively, his work addresses some of the most practically important challenges in deploying intelligent robots in everyday environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation71 citations · 2020
- 3
- 4VisuoSpatial Foresight for physical sequential fabric manipulation41 citations · 2021
- 5LazyDAgger: Reducing Context Switching in Interactive Imitation Learning29 citations · 2021
- 6VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation20 citations · 2020
- 7
- 8FogROS2-SGC: A ROS2 Cloud Robotics Platform for Secure Global Connectivity19 citations · 2023
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