Researchers have created a light-powered soft robot that can carry loads through the air along established tracks, similar to cable cars or aerial trams. The soft robot operates autonomously, can climb slopes at angles of up to 80 degrees, and can carry loads up to 12 times its weight.

“We’ve previously created soft robots that can move quickly through the water and across solid ground, but wanted to explore a design that can carry objects through the air across open space,” says Jie Yin, associate professor of mechanical and aerospace engineering at North Carolina State University and corresponding author of a paper on the work published in Advanced Science.

“The simplest way to do this is to follow an established track—similar to the aerial trams you see in the mountains. And we’ve now demonstrated that this is possible.”

At a time when we run ourselves ragged to meet society’s expectations of productivity, performance and time optimization, is it right that our robot vacuum cleaners and other smart appliances should sit idle for most of the day?

Computer scientists at the University of Bath in the UK think not. In a new paper, they propose over 100 ways to tap into the latent potential of our robotic devices. The researchers say these devices could be reprogrammed to perform helpful tasks around the home beyond their primary functions, keeping them physically active during their regular downtime.

New functions could include playing with the cat, watering plants, carrying groceries from car to kitchen, delivering breakfast in bed and closing windows when it rains.

Coordinating complicated interactive systems, whether it’s the different modes of transportation in a city or the various components that must work together to make an effective and efficient robot, is an increasingly important subject for software designers to tackle. Now, researchers at MIT have developed an entirely new way of approaching these complex problems, using simple diagrams as a tool to reveal better approaches to software optimization in deep-learning models.

They say the new method makes addressing these complex tasks so simple that it can be reduced to a drawing that would fit on the back of a napkin.

The new approach is described in the journal Transactions of Machine Learning Research, in a paper by incoming doctoral student Vincent Abbott and Professor Gioele Zardini of MIT’s Laboratory for Information and Decision Systems (LIDS).