This doesn’t really need much premise. A group of students at Zhejiang University in China created two robots, Wu and Kong, that can play table tennis with relative fluidity and reflex.

Wu and Kong are equipped with eye-mounted cameras that send 120 images per second to the robot’s processor, which calculates ball trajectory and landing position almost instantly. Then, this information is sent to the robot’s hydraulic joints, which respond to send the ball back.

The robots still have a long way to go. They aren’t fast enough, and can’t move from side to side, so they probably won’t be winning any table tennis tournaments in the near future. However, the original intent has been achieved: the robot is an effective training aid, because it is able to return the ball consistently to the same place.

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The concept of high-speed trains has become widely popular in the world, with the concept that the less friction there is, the higher speeds can be reached. Asia has already implemented these on a wide scale, but even these high-speed trains mostly run on rails, which give quite a bit of friction. The other type of low-friction vehicle is the maglev train, which relies on magnetic attraction and repulsion to hold a train above an electrical field using magnets, but even maglev trains have some amount of air drag, and they cost a colossal amount.

So, how else do you make a levitating vehicle? Well, the most obvious solution is air, since it’s between the ground and whatever object you have above it. In theory, this makes sense, but in practice, it’s quite a bit harder to do. First, to push enough air towards the ground at the right balance has to be carefully calculated. Also, one of the bigger difficulties that has arisen is that the ground-effect robot is controlled more like a plane than a train; in addition to the regular forward motion, the pitch, roll, and yaw must also be considered in its functionality.

Still, this technology is progressing fairly quickly, and hopefully the world will be seeing efficient ground-effect robot trains on the non-existent rails soon!

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While biorobotics has mostly been used to exploit the innate skills of living animals, in this case example, biorobots can also be used to model ecological situations, such as predator-prey relationships.

RoboSquirrel isn’t a complex robot. It mostly consists of two skills, first, moving from one end of a wooden box to the other, and second, to wag its tail. At first, it seems extremely useless; I mean, what does it even actually do? But after it is used on an actual rattlesnake, it become very clear what its function is.

From the video, we can clearly see that the rattlesnake does not strike when the squirrels tail moves, yet it does strike when the squirrels tail does not. Concepts such as these, which used to only be made through careful observation, can now rely on robots to model them effectively. And we find that the biorobot is actually effective when we take a look at an actual squirrels behavior.

It’s about three minutes long, and the first and last thirty seconds should give you what you want to know.

In this video, we see that for three minutes, a rattlesnake approaches a squirrel, but does not even think to strike, as the squirrel’s tail wags the entire time. While before the creation of the RoboSquirrel, this idea could only be theorized, the RoboSquirrel proves that it is innate and constant in its effect.

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