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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How a Microcontroller Hiding in a USB Port Became an FPGA Hiding in the Same

When you think of microcontroller development, you probably picture either a breadboard with a chip or a USB-connected circuit board. But Tim Ansell pictured an ARM dev board that is almost completely hidden inside of a USB port. His talk at the 2018 Hackaday Superconference tells that story and then some. Check out the newly published video, along with more details of the talk, after the break.

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A Scratch Instrument For Ants

If you think that this scratch instrument looks as though it should be at least… three times larger in order to be useful, you’d be wrong. This mighty pocket-sized instrument can really get the club hopping despite its diminuitive size. Despite that, the quality of the build as well as its use of off-the-shelf components for almost every part means that if you need a small, portable turntable there’s finally one you can build on your own.

[rasteri] built the SC1000 digital scratch instrument as a member of the portabilist scene, focusing on downsizing the equipment needed for a proper DJ setup. This instrument uses as Olimex A13-SOM-256 system-on-module, an ARM microprocessor, and can use a USB stick in order to load beats to the system. The scratch wheel itself uses a magnetic rotary encoder to sense position, and the slider is miniaturized as well.

If you want to learn to scratch good and learn to do other things good too, there’s a demo below showing a demonstration of the instrument, as well as a how-to video on the project page. All of the build files and software are open-source, so it won’t be too difficult to get one for yourself as long as you have some experience printing PCBs. If you need the rest of the equipment for a DJ booth, of course that’s also something you can build.

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A Pi Cluster to Hang in Your Stocking with Care

It’s that time of year again, with the holidays fast approaching friends and family will be hounding you about what trinkets and shiny baubles they can pretend to surprise you with. Unfortunately there’s no person harder to shop for than the maker or hacker: if we want it, we’ve probably already built the thing. Or at least gotten it out of somebody else’s trash.

But if they absolutely, positively, simply have to buy you something that’s commercially made, then you could do worse than pointing them to this very slick Raspberry Pi cluster backplane from [miniNodes]. With the ability to support up to five of the often overlooked Pi Compute Modules, this little device will let you bring a punchy little ARM cluster online without having to build something from scratch.

The Compute Module is perfectly suited for clustering applications like this due to its much smaller size compared to the full-size Raspberry Pi, but we don’t see it get used that often because it needs to be jacked into an appropriate SODIMM connector. This makes it effectively useless for prototyping and quickly thrown together hacks (I.E. everything most people use the Pi for), and really only suitable for finished products and industrial applications. It’s really the line in the sand between playing around with the Pi and putting it to real work.

[miniNodes] calls their handy little device the Carrier Board, and beyond the obvious five SODIMM slots for the Pis to live in, there’s also an integrated gigabit switch with an uplink port to get them all connected to the network. The board powers all of the nodes through a single barrel connector on the side opposite the Ethernet jack, leaving behind the masses of spider’s web of USB cables we usually see with Pi clusters.

The board doesn’t come cheap at $259 USD, plus the five Pi Compute Modules which will set you back another $150. But for the ticket price you’ll have a 20 core ARM cluster with 5 GB of RAM and 20 GB of flash storage in a 200 x 100 millimeter (8 x 4 inch) footprint, with an energy consumption of under 20 watts when running at wide open throttle. This could be an excellent choice for mobile applications, or if you just want to experiment with parallel processing on a desktop-sized device.

Amazon is ready for the coming ARM server revolution, are you? Between products like this and the many DIY ARM clusters we’ve seen over the years, it looks like we’re going to be dragging the plucky architecture kicking and screaming into the world of high performance computing.

[Thanks to Baldpower for the tip.]

Replace Legacy CNC PCs With A Gerbil

There are lots of laser cutters and other CNC machines available for a decent price online, but the major hurdle to getting these machines running won’t be the price or the parts. It’s usually the controller PC, which might be running Windows XP or NT if you’re lucky, but some of them are still using IBM XT computers from the ’80s. Even if the hardware in these machines is working, it might be impossible to get the software, and even then it will be dated and lacking features of modern computers. Enter the Super Gerbil.

[Paul] was able to find a laser cutter with one of these obsolete controllers, but figured there was a better way to getting it running again. As the name suggests, it uses GRBL, a G-Code parser and CNC controller software package that was originally made to run on an 8-bit AVR microcontroller, but [Paul] designed the Super Gerbil to run on a 32 bit ARM platform. He also added Z-axis control to it, so it now sports more degrees of freedom than the original software.

By way of a proof of concept, once he was finished building the Super Gerbil he ordered a CNC machine from China with an obsolete controller and was able to get it running within a day. As an added bonus, he made everything open so there are no license fees or cloud storage requirements if you want to use his controller. [Paul] also has a Kickstarter page for this project as well. Hopefully controllers haven’t been the only thing stopping you from getting a CNC machine for your lab, though, but if they have you now have a great solution for a 3040 or 3020 CNC machine’s controller, or any other CNC machine you might want to have. Continue reading “Replace Legacy CNC PCs With A Gerbil”

Getting Started with Free ARM Cores on Xilinx

We reported earlier about Xilinx offering free-to-use ARM Cortex M1 and M3 cores. [Adam Taylor] posted his experiences getting things working and there’s also a video done by [Geek Til It Hertz] based on the material that you can see in the second video, below.

The post covers using the Arty A35T or Arty S50 FPGA boards (based on Artix FPGAs) and the Xilinx Vivado software. Although Vivado will allow you to do conventional FPGA development, it also can work to compose function blocks to produce CPUs and that’s really what’s going on here.

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Amazon Thinks ARM is Bigger than your Phone

As far as computer architectures go, ARM doesn’t have anything to be ashamed of. Since nearly every mobile device on the planet is powered by some member of the reduced instruction set computer (RISC) family, there’s an excellent chance these words are currently making their way to your eyes courtesy of an ARM chip. A userbase of several billion is certainly nothing to sneeze at, and that’s before we even take into account the myriad of other devices which ARM processors find their way into: from kid’s toys to smart TVs.

ARM is also the de facto architecture for the single-board computers which have dominated the hacking and making scene for the last several years. Raspberry Pi, BeagleBone, ODROID, Tinker Board, etc. If it’s a small computer that runs Linux or Android, it will almost certainly be powered by some ARM variant; another market all but completely dominated.

It would be a fair to say that small devices, from set top boxes down to smartwatches, are today the domain of ARM processors. But if we’re talking about what one might consider “traditional” computers, such as desktops, laptops, or servers, ARM is essentially a non-starter. There are a handful of ARM Chromebooks on the market, but effectively everything else is running on x86 processors built by Intel or AMD. You can’t walk into a store and purchase an ARM desktop, and beyond the hackers who are using Raspberry Pis to host their personal sites, ARM servers are an exceptional rarity.

Or at least, they were until very recently. At the re:Invent 2018 conference, Amazon announced the immediate availability of their own internally developed ARM servers for their Amazon Web Services (AWS) customers. For many developers this will be the first time they’ve written code for a non-x86 processor, and while some growing pains are to be expected, the lower cost of the ARM instances compared to the standard x86 options seems likely to drive adoption. Will this be the push ARM needs to finally break into the server and potentially even desktop markets? Let’s take a look at what ARM is up against.

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