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.

As part of the 2013 DARPA Robotics Challenge, we are developing ladder- and stair- climbing skills for humanoid robots that may be used in disaster relief and industrial maintenance scenarios. Such tasks are challenging because they require upper-body strength, multi-limbed balance coordination, and fine motions for obstacle avoidance. We developed planners that can carefully deliberate about the robot's physical capabilities, and applied them planner-aided design to establish specifications for a new robot, DRC-Hubo. Using our software, DRC-Hubo is able to autonomously mount, climb, and dismount stairs and ladders. This project is conducted as part of Team HUBO, a multi-university team led by Drexel University.

The explosion in the amount of medical data available in Electronic Health Records (EHRs) provides an opportunity for computers to aid clinicians in providing effective, affordable healthcare. We are investigating Markov Decision Process techniques that are able to recommend patient-specific treatment plans in the presence of noisy and incomplete observations. Like a real doctor, the system is able to reason in the space of belief states, integrating uncertainties and contingencies into its plans. Results on a 5,807 patient dataset involving clinical depression as well as co-occuring conditions suggest that the decisions made by an AI system can improve patient outcomes from 30-35% while reducing costs by 50-55%.