February 04, 2019

Good Morning,

Okay...the robot and AI stuff is getting a little out of hand. Now, a new robot can create a "self-image". Like they don't have enough problems already.

Learn about this and more interesting stories from the scientific community in today's issue.

Until Next Time,
Erin

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*-- New robot can simulate a self-image, learn what it is --*

Engineers at Columbia University have built a robot capable of building an image of itself from scratch.

Robots have not yet learned to create a self-image. To conceptualize themselves, today's robots rely on human-installed models or time-consuming trial and error.

Within a day-and-a-half of intense computing, the new robot can learn its identity without any knowledge of physics, geometry or motor dynamics. The robot can use its self-simulation to adapt to a variety of novel environs and tasks, and can also use its self-image to initiate internal repairs.

"If we want robots to become independent, to adapt quickly to scenarios unforeseen by their creators, then it's essential that they learn to simulate themselves," Hod Lipson, professor of mechanical engineering at Columbia, said in a news release.

Lipson is the director of the Creative Machines lab, where he and his research partners designed and built the new robot.

Researchers started with a robotic arm with four degrees of freedom of motion. The robot's road toward pseudo-self-awareness began by moving randomly, logging 1,000 trajectories, each comprising 100 positional data points. Using the data, the robot built a self model.

Initially, the self-image wasn't accurate enough for the robot to identify itself and understand how its joints are connected. But using a deep-learning algorithm, the robot perfected its self model over the course of 35 hours. Finally, the robot produced a self-image accurate to within an inch-and-a-half.

Using an open loop system, which allows for an input signal, the robot was able to perform a pick-and-place task, retrieving objects and putting them in a receptacle with 100 percent accuracy. Using a closed loop system, relying solely on its internal self-image, the robot performed the pick-and-place task with 44 percent accuracy.

"That's like trying to pick up a glass of water with your eyes closed, a process difficult even for humans," PhD student Robert Kwiatkowski said.

When researchers attached a deformed component to the segmented arm, the robot successfully used itself self-image to identify the flaw and form an updated model of itself.

Researchers described their efforts in a new paper published this week in the journal Science Robotics.

"While our robot's ability to imagine itself is still crude compared to humans, we believe that this ability is on the path to machine self-awareness," Lipson said.

Lipson thinks that by continuing to build robots with greater levels of self-awareness, researchers can develop robotic systems with more autonomy and adaptability, capable of a broader array of problem solving.

In the future, these technological advances may warrant ethical considerations.

"Self-awareness will lead to more resilient and adaptive systems, but also implies some loss of control," Lipson and Kwiatkowski wrote in their newly published paper. "It's a powerful technology, but it should be handled with care."

*-- Scientists discover ideal wing shape for flight by simulating evolution --*

Scientists at New York University used a combination of computer simulations, 3D printing and lab tests to replicate evolution and identify the ideal wing shape for fast flapping flight.

"We can simulate biological evolution in the lab by generating a population of wings of different shapes, have them compete to achieve some desired objective, in this case, speed, and then have the best wings 'breed' to make related shapes that do even better," Leif Ristroph, an assistant professor at NYU's Courant Institute of Mathematical Sciences, said in a news release.

After their evolutionary models identified an array of wing shapes, scientists used a 3D printer to produce prototypes. Researchers raced the wings in NYU's Applied Math Lab. After identifying the fastest wing shapes, scientists used their genetic algorithm to "breed" the best shapes, producing offspring with a combination of beneficial aerodynamic attributes.

Their analysis showed the fastest shapes featured strong vortices at the wing's trailing edge that were not disrupted by the vortices produced at the wing's leading edge.

Scientists produced 15 generations of wings, each time using their algorithms to yield hybridized shapes and lab tests to identify the fastest parents with which to breed the next generation.

"This 'survival of the fastest' process automatically discovers a quickest teardrop-shaped wing that most effectively manipulates the flows to generate thrust," Ristroph said. "Further, because we explored a large variety of shapes in our study, we were also able to identify exactly what aspects of the shape were most responsible for the strong performance of the fastest wings."

Ristroph and his colleagues described their evolutionary simulation this week in the journal Proceedings of the Royal Society A.

The final wing shape, after 15 rounds of hybridization, features a razor-thin trailing edge, which leaves behind powerful eddies of swirling air.

"We view the work as a case study and proof-of-concept for a much broader class of complex engineering problems, especially those that involve objects in flows, such as streamlining the shape to minimize drag on a structure," said Ristroph. "We think this could be used, for example, to optimize the shape of a structure for harvesting the energy in water waves."