Just like humans, when robots have a decision to make there are often many options and hundreds of potential outcomes. Robots have been able to simulate a handful of these outcomes to figure out which course of action will be the most likely to lead to success. But what if one of the other options were equally likely to succeed--and safer? The Office of Naval Research has awarded Brendan Englot, an MIT-trained mechanical engineer at Stevens Institute of Technology, a 2020 Young Investigator Award of $508,693 to leverage a new variant of a classic artificial intelligence tool to allow robots to predict the many possible outcomes of their actions, and how likely they are to occur. The framework will allow robots to figure out which option is the best way to achieve a goal, by understanding which options are the safest, most efficient--and least likely to fail.

IMAGE: Brendan Englot at Stevens Institute of Technology will leverage a new variant of a classic artificial intelligence tools to create robots that can predict and manage the risks involved in... view more Just like humans, when robots have a decision to make there are often many options and hundreds of potential outcomes. Robots have been able to simulate a handful of these outcomes to figure out which course of action will be the most likely to lead to success. But what if one of the other options were equally likely to succeed - and safer? The Office of Naval Research has awarded Brendan Englot, an MIT-trained mechanical engineer at Stevens Institute of Technology, a 2020 Young Investigator Award of $508, 693 to leverage a new variant of a classic artificial intelligence tool to allow robots to predict the many possible outcomes of their actions, and how likely they are to occur. The framework will allow robots to figure out which option is the best way to achieve a goal, by understanding which options are the safest, most efficient - and least likely to fail.

Doctoral students at Stevens Institute of Technology hope the robot they're developing will be able to dive into waters and perform tasks that could be very dangerous for humans. "We would like the robot to be able to do infrastructure inspections, ideally to assess the integrity of underwater infrastructure, make sure everything is intact, working properly, that there are no damages or defects. Or potentially from a security standpoint, that there are no anomalies planted on an underwater piece of infrastructure," said Dr. Brendan Englot, professor of mechanical engineering. To do this, the robot must be able to understand its location in the water and be able to accurately find the structures it needs to assess. Through a process called machine learning, the robot gathers data and improves its own performance.

Algorithms created at Stevens Institute of Technology in New Jersey can teach robots to adapt to changing conditions related to protecting and preserving underwater infrastructure. Researchers at Stevens Institute of Technology in New Jersey have created algorithms to teach robots to adapt to changing conditions related to protecting and preserving underwater infrastructure. Stevens' Brendan Englot leads a group that uses reinforcement learning algorithms trained on sonar data. The group's robots emit high-frequency chirps and measure how long it takes the sound to return after reflecting off surrounding structures, gathering data and acquiring situational awareness while various forces buffet them. The research team recently dispatched a robot to autonomously map a Manhattan pier without a prior model.