4 citations · 6 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
Interleaving Optimization with Sampling-Based Motion Planning (IOS-MP): Combining Local Optimization with Global Exploration
Alan Kuntz, Chris Bowen, Ron Alterovitz
Computing globally optimal motion plans for a robot is challenging in part because it requires analyzing a robot's configuration space simultaneously from both a macroscopic viewpo…