Papers
34
Total Citations
934
H-Index
16
About
Ajay Kumar Tanwani is a robotics and machine learning researcher whose work spans robot learning, fabric manipulation, teleoperation, and distributed cloud-edge computing for intelligent systems. He is perhaps best known for his pioneering contributions to robotic fabric manipulation, where his deep imitation learning and visuospatial foresight frameworks have enabled robots to perform complex, multi-step tasks such as fabric smoothing, bed-making, and folding — work that has collectively garnered over 300 citations. His 2020 paper on sequential fabric smoothing alone has attracted 109 citations, reflecting its significant influence on the field. Tanwani has also made substantial contributions to probabilistic learning for robot manipulation, developing semitied hidden semi-Markov models that leverage spatial and temporal structure for robust task adaptation. His generative modeling work for teleoperation addresses critical challenges of latency and bandwidth in remote manipulation scenarios. Notably, he pioneered a "Fog Robotics" approach to distributed deep learning for robots, balancing privacy, security, and real-time performance across cloud and edge platforms. His contributions to dexterous underwater robotics through the DexROV project further demonstrate the breadth of his research, making him a versatile and impactful figure in modern robotics and human-robot interaction research.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4Deep Transfer Learning of Pick Points on Fabric for Robot Bed-Making81 citations · 2022
- 5VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation71 citations · 2020
- 6
- 7
- 8VisuoSpatial Foresight for physical sequential fabric manipulation41 citations · 2021
- 9RILaaS: Robot Inference and Learning as a Service35 citations · 2020
- 10