Bao Thach
Papers
6
Total Citations
59
H-Index
3
About
Bao Thach is a robotics researcher whose work spans deformable object manipulation, surgical robotics, and continuum robot modeling — areas at the intersection of deep learning and real-world robotic control. His most recognized contribution, "Learning Visual Shape Control of Novel 3D Deformable Objects from Partial-View Point Clouds" (2022, 34 citations), addresses the fundamental challenge of enabling robots to reshape elastic 3D objects without relying on complex analytical models, instead leveraging point cloud data and learned representations. This work builds on his earlier DeformerNet framework (2021), which introduced a deep neural network architecture for 3D deformable object manipulation, later extended to bimanual robotic settings in 2023. Thach has also made meaningful contributions to surgical automation, developing reward learning techniques from suboptimal demonstrations for electrocautery tasks, and advancing the kinematic modeling of tendon-driven continuum robots by accounting for hysteresis through deep decoder networks — work that directly supports safer, more precise minimally invasive surgery. With over 59 citations across his growing publication record, Thach represents an emerging voice in data-driven robotic manipulation, producing research with clear translational impact in healthcare and automation.
Research Focus
Key Achievements
Top Papers
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- 5DeformerNet: Learning Bimanual Manipulation of 3D Deformable Objects3 citations · 2023
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