10 citations · 13 across the 7 of their papers we have counts for
8 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…
Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics
Open-H-Embodiment Consortium, :, Nigel Nelson +213
Autonomous medical robots hold promise to improve patient outcomes, reduce provider workload, democratize access to care, and enable superhuman precision. However, autonomous medic…
Early Failure Detection in Autonomous Surgical Soft-Tissue Manipulation via Uncertainty Quantification
Jordan Thompson, Ronald Koe, Anthony Le +3
Autonomous surgical robots are a promising solution to the increasing demand for surgery amid a shortage of surgeons. Recent work has proposed learning-based approaches for the aut…
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…
Modeling Kinematic Uncertainty of Tendon-Driven Continuum Robots via Mixture Density Networks
Jordan Thompson, Brian Y. Cho, Daniel S. Brown +1
Tendon-driven continuum robot kinematic models are frequently computationally expensive, inaccurate due to unmodeled effects, or both. In particular, unmodeled effects produce unce…
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…
General-purpose foundation models for increased autonomy in robot-assisted surgery
Samuel Schmidgall, Ji Woong Kim, Alan Kuntz +2
The dominant paradigm for end-to-end robot learning focuses on optimizing task-specific objectives that solve a single robotic problem such as picking up an object or reaching a ta…
Efficient and Accurate Mapping of Subsurface Anatomy via Online Trajectory Optimization for Robot Assisted Surgery
Brian Y. Cho, Alan Kuntz
Robotic surgical subtask automation has the potential to reduce the per-patient workload of human surgeons. There are a variety of surgical subtasks that require geometric informat…