RI: Medium: Robust Intelligent Manipulation and Apprenticeship Learning for Robotic Surgical Assistants

Case Western Reserve University, Cleveland OH

Investigators

Abstract

"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)." This project aims to develop models, algorithms, and testbeds for robust intelligent manipulation that will enable supervisory control for robotic surgical assistants (RSAs). Drawing on the combined expertise of the investigators, the research is focusing on both analytic and empirical approaches. Using analytic approaches, the investigators are defining mathematical models of system state, deformable tissue dynamics, and stochastic uncertainty. These models are used to develop robotic motion planning and control algorithms to plan and perform grasping and manipulation of deformable objects under uncertainty. Using empirical approaches, the investigators are developing machine learning techniques to efficiently find dynamic control policies and characterize performance metrics based on expert demonstrations. Simulation and hardware testbeds for a common set of benchmark problems are being developed to evaluate these algorithms and methods. This project will advance basic scientific understanding by developing new analytic methods for robust grasping and manipulation of deformable objects. This project will also advance empirical approaches by developing controllers that learn deformable object manipulation skills by observing expert demonstrations. The project will extend, compare, and evaluate analytic and empirical methods and seek to develop new hybrid methods within the focused context of providing robust intelligence for RSAs. Robust intelligent manipulation for RSAs will improve patient health and reduce costs by enhancing surgeon performance, reducing tedium, and decreasing operation time. Outreach to local girls' high schools and predominantly minority Cleveland high schools is pursued as part of the project to encourage participation of underrepresented groups in engineering and computer science.