Drones and swarm robotics: Adaptive capabilities for human-robot interaction

Professor Julie A. Adams and her team of roboticists are hard at work developing a set of adaptive capabilities for human-robot teaming. In a recent interview, Adams explained her team’s focus on swarm robotics, where hundreds of robots are deployed simultaneously. The team’s goal is to enable a single human to deploy and control a robot swarm while maintaining a normal workload without being overburdened.

With sim-to-real, Cassie sprints toward a new engineering paradigm

Cassie the bipedal robot recently earned a spot in the Guinness Book of World Records for being the fastest two-legged robot on Earth, running the 100-meter dash in just under 25 seconds. The feat is especially impressive, considering Cassie pulled it off blind, without an onboard camera. Instead, Cassie first learned how to run through a series of “sim-to-real” training sessions.

AgAID: Tackling Agriculture with AI

The possibility of losing a crop to frost keeps farmers awake at night. Similarly, the decision to employ frost mitigation solutions, which can prevent crop loss from unseasonably cold temperatures, is also not taken lightly. Ranging in cost from hundreds of thousands of dollars up into the millions, the question “to mitigate or not?” is as much of a gamble as trying to predict the stock market.

Xiao Fu earns NSF CAREER Award

Xiao Fu, assistant professor of electrical and computer engineering and artificial intelligence, has received a Faculty Early Career Development, or CAREER, award from the National Science Foundation. Fu will use his five-year, $500,000 award to develop a suite of nonlinear factor analysis tools and contribute to a deeper understanding of unsupervised machine learning and sensing systems. 

Pulling back the curtain on neural networks

When researchers at Oregon State University created new tools to evaluate the decision-making algorithms of an advanced artificial intelligence system, study participants assigned to use them did, indeed, find flaws in the AI’s reasoning. But once investigators instructed participants to use the tools in a more structured and rigorous way, the number of bugs they discovered increased markedly.