When it comes to training robots to perform agile, single-task motor skills, such as handstands or backflips, artificial intelligence methods can be very useful. But if you want to train your robot to perform multiple tasks—say, performing a backward flip into a handstand—things get a little more complicated.

“We often want to train our robots to learn new skills by compounding existing skills with one another,” said Ian Abraham, assistant professor of mechanical engineering. “Unfortunately, AI models trained to allow robots to perform complex skills across many tasks tend to have worse performance than training on an individual task.”

To solve for that, Abraham’s lab is using techniques from optimal control—that is, taking a mathematical approach to help robots perform movements in the most efficient and optimal way possible. In particular, they’re employing hybrid control theory, which involves deciding when an autonomous system should switch between control modes to solve a task. The research is published on the arXiv preprint server.

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AlphaEvolve, an AI system created by Google DeepMind, is helping mathematicians do research at a scale that was previously impossible — even if it does occasionally “cheat” to find a solution

Large language models (LLMs) like ChatGPT can write an essay or plan a menu almost instantly. But until recently, it was also easy to stump them. The models, which rely on language patterns to respond to users’ queries, often failed at math problems and were not good at complex reasoning. Suddenly, however, they’ve gotten a lot better at these things.

A new generation of LLMs known as reasoning models are being trained to solve complex problems. Like humans, they need some time to think through problems like these—and remarkably, scientists at MIT’s McGovern Institute for Brain Research have found that the kinds of problems that require the most processing from reasoning models are the very same problems that people need to take their time with.

In other words, they report in the journal PNAS, the “cost of thinking” for a reasoning model is similar to the cost of thinking for a human.