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”

MIT’s Kino provides Kinetic, “living” jewelry for dynamic fashion

MIT’s Kino project explores a dynamic future in which the accessories we wear are no longer static, but are instead mobile, living objects on the body. Engineered with the functionality of miniaturized robotics, this “living” jewelry roams on unmodified clothing, changing location and reconfiguring appearance according to social context and enabling multiple presentations of self. … Continue reading “MIT’s Kino provides Kinetic, “living” jewelry for dynamic fashion”

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 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”