Our lab is developing the Tele-Robotic Intelligent Nursing Assistant (TRINA), a remote-controlled robot to perform common nursing duties inside hazardous clinical areas, which could reduce infection risk to healthcare workers by minimizing exposure to contagions and other biohazards. They could also reduce infection risk to immunocompromise patients. Apart from the engineering challenge, we are addressing scientific questions regarding human-robot interfaces, haptics, and semi-autonomous control and planning algorithms.

This project explores the hypothesis that advances in robot motion planning algorithms will lead to improved intuitiveness, safety, and task performance of human-centered robots such as intelligent vehicles, tele-surgery systems, search-and-rescue robots, and household robots. Existing planning techniques lead to awkward and unintuitive interaction with humans. To bridge this gap, we are developing cooperative motion planning algorithms that reason about users' intended goals and then take control of a robot's low level motion to achieve those goals.

Recently, one area that has attracted a lot of research attention is automated packaging or packing, a process during which robots stow objects into small confined spaces, such as shipping boxes. Packing items densely improves the storage capacity, decreases the delivery cost, and saves packing materials. This project focuses on packing objects of arbitrary shapes, weights, and deformation into a single shipping box with a robot manipulator. We seek to advance the state-of-the-art in robot packing with regards to optimizing container size for a set of objects, planning object placements for stability and feasibility, and increasing robustness of packing execution with a robot manipulator.

The large motion planning problems that arise in robotics, CAD/CAM, animation of virtual characters, and surgical planning are very challenging: they require searching high-dimensional state spaces with complex geometric constraints, nonlinear dynamics, often with contact and impact, and long time horizons. Prior approaches to these problems either exploit problem structure and/or embed a great deal of domain knowledge by hand into a planner. But this process is tedious, error-prone, and does not scale well to harder problems whose structure is non-obvious. This project will investigate automated strategies for planning systems to automatically discover common solution structures from past experience and to reuse this knowledge in new problems.