Scientists use a 3D printer and a hammer to find out
by Mary Beth Griggs
We’ve got a lot to learn from a seahorse’s tail. Unlike other
animals, these fishes’ tails are square, not round–a fundamental
difference in shape that scientists believe could lead to new
developments in medicine, robotics, and even defense.
In a paper published today in Science, researchers found that the difference in shape actually made a huge difference in how resilient the seahorse’s tail is.
order to figure out why the seahorse tail is square while so many other
animal’s tails are round (rats, lizards, monkeys, cats, etc.),
scientists printed out 3D replicas of the square tails and similarly
sized round tails.
“The story gained some morbid attention earlier today when a Financial Times
employment reporter named Sarah O’Connor tweeted the story, not
realizing the connection between her name and character who has a
similar name (Sarah Connor) in the Terminator series. Her tweet was retweeted more than 3,500 times and
she received an influx of messages making jokes about the news.”
Visual portfolio reel of various projects related to self-assembly robotics which could apply to architecture - a good primer on current ideas in the field:
Research from the AADRL Spyropoulos Design Lab exploring an architecture
that is self-aware, self-structured and self-assembles. The research
explores high population of mobility agents that evolve an architecture
that moves beyond the fixed and finite towards a behavioural model of
interactive human and machine ecologies.
You can find out more about the AA DRL program at their website here
USA Builds Giant Piloted Robot, Challenges Japan to a Robot Duel
If the challenge is accepted, MegaBots may take “America’s first fully-functional giant piloted robot” called the MegaBot Mark II into the battle.
The 15-foot-tall mech is equipped with guns that can fire three-pound paint cannonballs at more than 120 miles per hour. The current bipedal mech gets around on caterpillar treads; however, the team is a designing full-scale walking robot.+
researchers have developed algorithms that enable robots to learn motor
tasks through trial and error using a process that more closely
approximates the way humans learn. This marks a major milestone in the
field of artificial intelligence.
Motor learning is much more challenging than passive recognition of images and sounds.
demonstrated their technique, a type of reinforcement learning, by
having a robot complete various motor tasks without pre-programmed
details about its surroundings.
“Most robotic applications are in
controlled environments where objects are in predictable positions,”
said UC Berkeley faculty member Trevor Darrell, who is one of the leads
on the project.
“The challenge of putting robots into
real-life settings, like homes or offices, is that those environments
are constantly changing. The robot must be able to perceive and adapt to
its surroundings,” said Darrell.
impractical approaches to helping a robot make its way through a 3D
world include pre-programming it to handle the vast range of possible
scenarios or creating simulated environments within which the robot
Instead, the researchers turned to a new branch of
artificial intelligence known as deep learning, which is loosely
inspired by the neural circuitry of the human brain when it perceives
and interacts with the world.
Deep learning helps the robot recognize patterns and categories among the sensory data it is receiving.