Papers
12
Total Citations
382
H-Index
9
About
Aditya Ganapathi is a robotics researcher specializing in deformable object manipulation, robot learning, and computer vision, with a particular focus on the notoriously difficult challenge of autonomous fabric manipulation. His most influential work explores how robots can learn to smooth, fold, and manipulate textiles in real-world settings — tasks with broad applications in home robotics, healthcare, and manufacturing. His 2020 paper on deep imitation learning for sequential fabric smoothing (109 citations) demonstrated that learning from algorithmic supervisors using RGB-D inputs could yield effective pulling policies for flattening fabrics. Complementing this, his VisuoSpatial Foresight series (collectively exceeding 130 citations) extended predictive visual learning frameworks to enable multi-step, multi-task fabric manipulation without task-specific engineering. Ganapathi has also advanced sim-to-real transfer through dense visual correspondence learning, enabling policies trained on synthetic data to generalize to physical fabrics. His 2022 cloud robotics study on garment folding highlighted scalable evaluation infrastructure for deformable manipulation research. More recently, his work on implicit kinematic policies addresses fundamental questions in action space representation for end-to-end robot learning, reflecting a broadening research vision beyond fabric-specific domains.
Research Focus
Key Achievements
Top Papers
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- 2VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation71 citations · 2020
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- 4VisuoSpatial Foresight for physical sequential fabric manipulation41 citations · 2021
- 5VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation20 citations · 2020
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