Enlarge / An interdisciplinary team of roboticists, engineers and biologists modeled the mechanics of the mantis shrimp’s punch and built a robot that mimics the movement. (credit: Second Bay Studios and Roy Caldwell/Harvard SEAS)

The mantis shrimp boasts one of the most powerful, ultrafast punches in nature—it's on par with the force generated by a .22 caliber bullet. This makes the creature an attractive object of study for scientists eager to learn more about the relevant biomechanics. Among other uses, it could lead to small robots capable of equally fast, powerful movements. Now a team of Harvard University researchers have come up with a new biomechanical model for the mantis shrimp's mighty appendage, and they built a tiny robot to mimic that movement, according to a recent paper published in the Proceedings of the National Academy of Sciences (PNAS).

“We are fascinated by so many remarkable behaviors we see in nature, in particular when these behaviors meet or exceed what can be achieved by human-made devices,” said senior author Robert Wood, a roboticist at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). “The speed and force of mantis shrimp strikes, for example, are a consequence of a complex underlying mechanism. By constructing a robotic model of a mantis shrimp striking appendage, we are able to study these mechanisms in unprecedented detail.”

Wood's research group made headlines several years ago when they constructed RoboBee , a tiny robot capable of partially untethered flight. The ultimate goal of that initiative is to build a swarm of tiny interconnected robots capable of sustained untethered flight—a significant technological challenge, given the insect-sized scale, which changes the various forces at play. In 2019, Wood's group announced their achievement of the lightest insect-scale robot so far to have achieved sustained, untethered flight—an improved version called the RoboBee X-Wing. (Kenny Breuer, writing in Nature, described it as a "a tour de force of system design and engineering.")

Enlarge / The robotic train. (credit: Oliveri et. al.)

One of the most impressive developments in recent years has been the production of AI systems that can teach themselves to master the rules of a larger system. Notable successes have included experiments with chess and Starcraft . Given that self-teaching capability, it's tempting to think that computer-controlled systems should be able to teach themselves everything they need to know to operate. Obviously, for a complex system like a self-driving car, we're not there yet. But it should be much easier with a simpler system, right?

Maybe not. A group of researchers in Amsterdam attempted to take a very simple mobile robot and create a system that would learn to optimize its movement through a learn-by-doing process. While the system the researchers developed was flexible and could be effective, it ran into trouble due to some basic features of the real world, like friction.

Roving robots

The robots in the study were incredibly simple and were formed from a varying number of identical units. Each had an on-board controller, battery, and motion sensor. A pump controlled a piece of inflatable tubing that connected a unit to a neighboring unit. When inflated, the tubing generated a force that pushed the two units apart. When deflated, the tubing would pull the units back together.

Enlarge (credit: Emanuele Cremaschi | Getty Images)

Amazon is rolling out cheap new tools that will allow factories everywhere to monitor their workers and machines, as the tech giant looks to boost its presence in the industrial sector.

Launched by Amazon’s cloud arm AWS, the new machine learning-based services include hardware to monitor the health of heavy machinery, and computer vision capable of detecting whether workers are complying with social distancing.

Amazon said it had created a two-inch, low-cost sensor—Monitron—that can be attached to equipment to monitor abnormal vibrations or temperatures and predict future faults.