Robot’s Actions And Our Reactions

If you walk into a dog owner’s home that dog is probably going to make a beeline to see if you are a threat. If you walk into a cat owner’s home, you may see the cat wandering around, if it even chooses to grace you with its presence. For some people, a dog’s direct approach can be nerve-wracking, or even scary depending on their history and relative size of the dog. Still, these domestic animals are easy to empathize with especially if you or your family have a pet. They have faces which can convey curiosity or smug indifference but what if you were asked to judge the intent of something with no analogs to our own physical features like a face or limbs? That is what researchers at the IDC Herzliya in Israel and Cornell University in the US asked when they made the Greeting Machine to move a moon-like sphere around a planet-like sphere.

Participants were asked to gauge their feelings about the robot after watching the robot move in different patterns. It turns out that something as simple as a sphere tracing across the surface of another sphere can stir consistent and predictable emotions in people even though the shapes do not resemble a human, domestic pet, or anything but a snowman’s abdomen. This makes us think about how our own robots must be perceived by people who are not mired in circuits all day. Certainly, a robot jellyfish lazing about in the Atlantic must feel less threatening than a laser pointer with a taste for human eyeballs.

 

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You Won’t Hear This Word On The Street

The simplest answer to a problem is not necessarily always the best answer. If you ask the question, “How do I get a voice assistance to work on a crowded subway car?”, the simplest answer is to shout into a microphone but we don’t want to ask Siri to put toilet paper on the shopping list in front of fellow passengers at the top of our lungs. This is “not a technical issue but a mental issue” according to [Masaaki Fukumoto], lead researcher at Microsoft in “hardware and devices” and “human-computer interaction.” SilentVoice was demonstrated in Berlin at the ACM Symposium on User Interface Software and Technology which showed a live transcription of nearly silent speech. A short demonstration can be found below the break.

SilentVoice relies on a different way of speaking and a different way of picking up that sound. Instead of traditional dictation in which we exhale while facing a microphone, it is necessary to place the microphone less than two millimeters from the mouth, usually against the lips, and use ingressive speech which is just whispering while inhaling. The advantage of ingressive over egressive speech is that without air being blown over the microphone, the popping of air gusts is eliminated. With practice, it is as efficient as normal speaking but that practice will probably involve a few dizzy spells from inhaling more than necessary.

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Robot Arm Is A Fast Learner

Not long ago, machines grew their skills when programmers put their noses to the grindstone and mercilessly attacked those 104 keys. Machine learning is turning some of that around by replacing the typing with humans demonstrating the actions they want the robot to perform. Suddenly, a factory line-worker can be a robot trainer. This is not new, but a robot needs thousands of examples before it is ready to make an attempt. A new paper from researchers at the University of California, Berkeley, are adding the ability to infer so robots can perform after witnessing a task just one time.

A robotic arm with no learning capability can only be told to go to (X,Y,Z), pick up a thing, and drop it off at (X2, Y2, Z2). Many readers have probably done precisely this in school or with a homemade arm. A learning robot generates those coordinates by observing repeated trials and then copies the trainer and saves the keystrokes. This new method can infer that when the trainer picks up a piece of fruit, and drops it in the red bowl, that the robot should make sure the fruit ends up in the red bowl, not just the location where the red bowl was before.

The ability to infer is built from many smaller lessons, like moving to a location, grasping, and releasing and those are trained with regular machine learning, but the inference is the glue that holds it all together. If this sounds like how we teach children or train workers, then you are probably thinking in the right direction.

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Portable Pi Teensy Thumboard

Even on the go, there is no substitute for a physical keyboard with buttons that move and click. Sure, you could solder a bunch of tactile switches to some perfboard, but how about going all out and making something robust as [Anthony DiGirolamo] did for his Teensy Thumboard. Everything is insertion-mount so it is an approachable project for anyone who knows the dangerous end of a soldering iron, and that also makes it easy to hack on.

Each pin of the Teensy has an adjacent empty hole tied to it for easy access, and the serial data pins are exposed at the top of the board. All the holes use standard 0.1″ (2.54mm) spacing. The I/O points used by the keyboard are labeled, and the rest of them can use the space under the controller where proto-board style holes add some extra space for an IMU or whatever sensors suit your slant.

Most impressive is the shell, which is freely available on Thingiverse, where you can also find a bill of materials with links to everything you will need in case you don’t have drawers full of those tactile switches.

If this looks familiar, you have probably seen the PocketCHIP, and it is no secret that this project is an homage to that versatile pocket computer. We appreciate this kind of love for PocketCHIP, especially since they are now a limited commodity.

Manhole Covers Hide Antennas

5G is gearing up to be the most extensive implementation of mesh networking ever, and that could mean antennas will not need to broadcast for miles, just far enough to reach some devices. That unsightly cell infrastructure stuck on water towers and church steeples could soon be hidden under low-profile hunks of metal we are already used to seeing; manhole covers. This makes sense because 5G’s millimeter radio waves are more or less line-of-sight, and cell users probably wouldn’t want to lose connectivity every time they walk behind a building.

At the moment, Vodafone in the UK is testing similar 4G antennas and reaching 195 megabits/sec download speeds. Each antenna covers a 200-meter radius and uses a fiber network because, courtesy of existing underground infrastructure. There is some signal loss from transmitting and receiving beneath a slab of metal, but that will be taken into account when designing the network. The inevitable shift to 5G will then be a relatively straightforward matter of lifting the old antennas out and laying the new hardware inside, requiring only a worker and a van instead of a construction crew.

We want to help you find all the hidden cell phone antennas and pick your own cell module.

Via IEEE Spectrum.

Tech Tattoos Trace Two Dimensions

Flexible circuit boards bend as you might expect from a playing card, while skin stretches more like knit fabric. The rules for making circuit boards and temporary tattoos therefore need to be different. Not just temporary tattoos, there are also circuits that reside on the skin so no unregulated heat traces, please. In addition to flexing and stretching, these tattoos can be applied to uneven surfaces and remain intact. Circuits could be added to the outside of projects or use the structure as the board to reduce weight and size. Both are possible with the research from Carnegie Mellon’s Soft Machines Lab and the Institute of Systems and Robotics at the University of Coimbra.

These circuits are an improvement over the existing method which relies on cropping away metal foil with a magnifying glass, tweezers and a steady hand. Instead, silver particles are printed with an inkjet printer before the traces are coated in eutectic gallium indium which is liquid metal at room temperature. If we were to oversimplify, we might describe it as similar to a non-toxic equivalent of mercury that we have also seen used in DIY OLEDs. This is a development likely to be interesting in a range of fields from medicine to cosplay.

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Artificial Limbs And Intelligence

Prosthetic arms can range from inarticulate pirate-style hooks to motorized five-digit hands. Control of any of them is difficult and carries a steep learning curve, rarely does their operation measure up to a human arm. Enhancements such as freely rotating wrist might be convenient, but progress in the field has a long way to go. Prosthetics with machine learning hold the promise of a huge step to making them easier to use, and work from Imperial College London and the University of Göttingen has made great progress.

The video below explains itself with a time-trial where a man must move clips from a horizontal bar to a nearby vertical bar. The task requires a pincer grasp and release on the handles, and rotation from the wrist. The old hardware does not perform the two operations simultaneously which seems clunky in comparison to the fluid motion of the learning model. User input to the arm is through electromyography (EMG), so it does not require brain surgery or even skin penetration.

We look forward to seeing this type of control emerging integrated with homemade prosthetics, but we do not expect them to be easy.

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