The following is a summary of a series of stories appearing on Pratt School of Engineering .
These Pratt School of Engineering faculty members are studying how advanced systems are learning to navigate, understand and interact with the physical world.
Building in Security
One exciting possibility of physical AI is developing teams of autonomous vehicles and drones that can cooperate in real time. Imagine autonomous aircraft searching a disaster zone. Miroslav Pajic , a professor in the Department of Electrical and Computer Engineering, says the idea has great potential but also comes with vulnerabilities.
To avoid such attacks, Pajic says security should not be an afterthought; such features should be built in from the start.
Read the full story on Pratt School of Engineering .
What Robots Can Learn
Embedding AI into robots is changing how we interact with them. Instead of programming every behavior, "we can build robots that learn from their own experiences, develop internal models of themselves and their surroundings, and adapt when the world does not match what their designers expected," says Boyuan Chen, Dickinson Family Assistant Professor of Mechanical Engineering and Materials Science.
His lab studies how a robot's physical form shapes what it can perceive, learn, and do.
Lear more about robotics on Pratt School of Engineering .
Complex Decision-Making
For Yiran Chen , the John Cocke Distinguished Professor of Electrical and Computer Engineering, the challenge is finding ways to build systems that are efficient and powerful enough to run on resource-constrained devices while reliably supporting complex decision-making.
Efficiency, functionality, adaptability and trustworthiness are key components of physical AI systems, says Chen.
Physical AI systems must be able to see their surroundings and make decisions in real time, even when their cloud connection is unreliable.
