4D Camera Could Improve Robot Vision, Virtual Reality and Self-driving Cars

Engineers at Stanford University and the University of California San Diego have developed a camera that generates four-dimensional images and can capture 138 degrees of information. The new camera — the first-ever single-lens, wide field of view, light field camera — could generate information-rich images and video frames that will enable robots to better navigate … Continue reading “4D Camera Could Improve Robot Vision, Virtual Reality and Self-driving Cars”

Stanford’s Vinebot can grow without moving its whole body

Stanford researchers have developed a new type of soft, growing robot. This newly developed vine-like robot can grow across long distances without moving its whole body. It could prove useful in search and rescue operations and medical applications. Inspired by natural organisms that cover distance by growing – such as vines, fungi and nerve cells … Continue reading “Stanford’s Vinebot can grow without moving its whole body”

Helping Robots Learn to See in 3-D

Autonomous robots can inspect nuclear power plants, clean up oil spills in the ocean, accompany fighter planes into combat and explore the surface of Mars. Yet for all their talents, robots still can’t make a cup of tea. That’s because tasks such as turning the stove on, fetching the kettle and finding the milk and … Continue reading “Helping Robots Learn to See in 3-D”

MIT’s Flying Car – A Drone that can both Fly and Drive

Many birds and insects have the ability to both walk and take flight If we could program robots with similar versatility, it would open up many possibilities: Imagine machines that could fly into construction areas or disaster zones that aren’t near roads and then squeeze through tight spaces on the ground to transport objects or … Continue reading “MIT’s Flying Car – A Drone that can both Fly and Drive”

MIT’s C-LEARN allows Robots to learn Tasks easily from other Robots

Most robots are programmed using one of two methods: One method is, learning from demonstration, in which they watch a task being done and then replicate it. The other method is, via motion-planning techniques such as optimization or sampling, which require a programmer to explicitly specify a task’s goals and constraints. Both methods have drawbacks. … Continue reading “MIT’s C-LEARN allows Robots to learn Tasks easily from other Robots”