10 citations · 18 across the 9 of their papers we have counts for
10 papers
Bayesian Retraction Optimization for Tissue Attachment Mapping in Surgical Dissection
Shing-Hei Ho, Bao Thach, Toan Vo +2
With growing surgeon shortages, automating surgical sub-tasks such as tissue dissection offers a promising step toward reducing workload and expanding patient access. Prior work ha…
FOCA: Future-Oriented Conditioning for Data-Efficient Vision-Language-Action Adaptation
Duc Minh Nguyen, Nghiem Tuong Diep, Binh Gia Nguyen +20
Vision-Language-Action (VLA) models enable general-purpose robotic control via large-scale multimodal pretraining, yet their effectiveness under few-shot imitation learning remains…
DiffDef: A Diffusion Model for Generating Multimodal Goal Shapes From Demonstrations for Deformable Object Manipulation
Bao Thach, Tanner Watts, Siyeon Kim +6
Deformable object manipulation is a key capability in many robotic applications. A promising paradigm for this problem is shape servoing, which aims to control deformable objects t…
LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes
Shing-Hei Ho, Bao Thach, Minghan Zhu
We present LiDAR-EDIT, a novel paradigm for generating synthetic LiDAR data for autonomous driving. Our framework edits real-world LiDAR scans by introducing new object layouts whi…
Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery
Zohre Karimi, Shing-Hei Ho, Bao Thach +2
Automating robotic surgery via learning from demonstration (LfD) techniques is extremely challenging. This is because surgical tasks often involve sequential decision-making proces…
Accounting for Hysteresis in the Forward Kinematics of Nonlinearly-Routed Tendon-Driven Continuum Robots via a Learned Deep Decoder Network
Brian Y. Cho, Daniel S. Esser, Jordan Thompson +3
Tendon-driven continuum robots have been gaining popularity in medical applications due to their ability to curve around complex anatomical structures, potentially reducing the inv…
DefGoalNet: Contextual Goal Learning from Demonstrations For Deformable Object Manipulation
Bao Thach, Tanner Watts, Shing-Hei Ho +2
Shape servoing, a robotic task dedicated to controlling objects to desired goal shapes, is a promising approach to deformable object manipulation. An issue arises, however, with th…
DeformerNet: Learning Bimanual Manipulation of 3D Deformable Objects
Bao Thach, Brian Y. Cho, Shing-Hei Ho +2
Applications in fields ranging from home care to warehouse fulfillment to surgical assistance require robots to reliably manipulate the shape of 3D deformable objects. Analytic mod…
Learning Visual Shape Control of Novel 3D Deformable Objects from Partial-View Point Clouds
Bao Thach, Brian Y. Cho, Alan Kuntz +1
If robots could reliably manipulate the shape of 3D deformable objects, they could find applications in fields ranging from home care to warehouse fulfillment to surgical assistanc…
DeformerNet: A Deep Learning Approach to 3D Deformable Object Manipulation
Bao Thach, Alan Kuntz, Tucker Hermans
In this paper, we propose a novel approach to 3D deformable object manipulation leveraging a deep neural network called DeformerNet. Controlling the shape of a 3D object requires a…