We are working to elevate robots from mechanical creations controlled by low-level scripts with a considerable amount of human guidance to truly cognitive robots.
There are three main thrusts to the research in the Model-Based Embedded and Robotics Systems (MERS) group: goal-driven interaction with robots, natural human/robot teaming, and robotic reasoning about the environment.
When combined, these research topics allow us to create cognitive robots that can be talked to like another human, can work with a team member to finish a task, can recover from many failures without assistance, and can collaborate with a human to recover from a failure that the robot cannot solve alone.
We enable these cognitive robot abilities by using model-based techniques. At the heart of these techniques are engineering models of how the robot works and models of how the robot’s environment behaves.
On top of these models, we have developed algorithms that enable the robot to reason over how it believes the world works, much like humans do.
Our goal is to create an online risk-aware planner for vehicle maneuvers that can make driving safer and less stressful through a “parallel” autonomous system that assists the driver by watching for risky situations, and by helping the driver take proactive, compensating actions before they become crises.
Uhura is an autonomous system that collaborates with humans in planning and executing complex tasks, especially under over-subscribed and risky situations.
Using AI methods, we are developing an attack tree generator that automatically enumerates cyberattack vectors for industrial control systems in critical infrastructure (electric grids, water networks and transportation systems). The generator can quickly assess cyber risk for a system at scale.
IoT devices primarily use free embedded Linux which has many security flaws. We are conducting penetration tests on IoT and developing a secure version of embedded Linux.
An AI team coordinator aligns agents’ beliefs about how to achieve a task, intervening when necessary to potentially help with tasks in search and rescue, hospitals, and video games.
A new technique can safely guide an autonomous robot without knowledge of its environmental conditions or the size, shape, or location of obstacles it might encounter.
