Training Machines to Learn More Like Humans Do

Training computer Vision models

MIT News, By Adam Zewe  May 9, 2023

Scientists at the Massachusetts Institute of Technology (MIT) and Toyota subsidiary Woven Planet found computer vision models can be trained to produce more stable, predictable visual representations, similar to those humans learn through perceptual straightening. The researchers taught the models millions of examples via adversarial training, which enhanced their perceptual straightness while reducing their reactivity to slight errors within images. They discovered the models trained on more perceptually straight representations could correctly classify objects in videos with greater consistency. MIT's Vasha DuTell said, "One of the take-home messages here is that taking inspiration from biological systems, such as human vision, can both give you insight about why certain things work the way that they do and also inspire ideas to improve neural networks."

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Robots Learn Human Trick

Quite interesting piece, surely they should learn from us.

Robot Learns Human Trick for Not Falling Over Humanoid limbs are useful for more than just manipulation

By Evan Ackerman in IEEE,  Image:   A black and white humanoid robot with a malfunctioning leg supports itself with one arm against a wall

This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore.

Humanoid robots are a lot more capable than they used to be, but for most of them, falling over is still borderline catastrophic. Understandably, the focus has been on getting humanoid robots to succeed at things as opposed to getting robots to tolerate (or recover from) failing at things, but sometimes, failure is inevitable because stuff happens that’s outside your control. Earthquakes, accidentally clumsy grad students, tornadoes, deliberately malicious grad students—the list goes on.

When humans lose their balance, the go-to strategy is a highly effective one: use whatever happens to be nearby to keep from falling over. While for humans this approach is instinctive, it’s a hard problem for robots, involving perception, semantic understanding, motion planning, and careful force control, all executed under aggressive time constraints. In a paper published earlier this year in IEEE Robotics and Automation Letters, researchers at Inria in France show some early work getting a TALOS humanoid robot to use a nearby wall to successfully keep itself from taking a tumble.   ... '