Robodog Goes Free Thanks To Unofficial SDK

What’s better than a pretty nice legged robot? One with an alternate SDK version that opens up expensive features, of course. The author didn’t like that the original SDK only came as pre-compiled binaries restricted to the most expensive models, so rolled up their sleeves and started writing a new one.

The manufacturer’s SDK limits access to programmatic functions, but that needn’t stop you.

There are a number of commercially-available robotic quadrupeds that can trace their heritage back to the MIT Mini Cheetah design, and one of them is the Unitree Go1 series which sports a distinctive X-shaped sensor cluster on its “face”. The basic models are affordable (as far as robots go, anyway) but Unitree claims only the high-priced EDU model can be controlled via the SDK. Happily, the Free Dog SDK provides a way to do exactly that.

The SDK is a work in progress, but fully usable and allows the user to send various high level and low level commands to the Go1 robots. High level examples include things like telling the robot to perform pushups, turn 90 degrees, or walk. Low level commands are things like specifying exact positions or torque levels for individual limbs. With the new SDK, doing those things programmatically is only a Python script away.

Know any other robots that might be based on the same system? This SDK might work on them, too.

In Praise Of RPN (with Python Or C)

HP calculators, slide rules, and Forth all have something in common: reverse polish notation or RPN. Admittedly, slide rules don’t really have RPN, but you work problems on them the same way you do with an RPN calculator. For whatever reason, RPN didn’t really succeed in the general marketplace, and you might wonder why it was ever a thing. The biggest reason is that RPN is very easy to implement compared to working through proper algebraic, or infix, notation. In addition, in the early years of computers and calculators, you didn’t have much to work with, and people were used to using slide rules, so having something that didn’t take a lot of code that matched how users worked anyway was a win-win.

What is RPN?

If you haven’t encountered RPN before, it is an easy way to express math without ambiguity. For example, what’s 5 + 3 * 6?  It’s 23 and not 48. By order of operations you know that you have to multiply before you add, even if you wrote down the multiplication second. You have to read through the whole equation before you can get started with math, and if you want to force the other result, you’ll need parentheses.

With RPN, there is no ambiguity depending on secret rules or parentheses, nor is there any reason to remember things unnecessarily. For instance, to calculate our example you have to read all the way through once to figure out that you have to multiply first, then you need to remember that is pending and add the 5. With RPN, you go left to right, and every time you see an operator, you act on it and move on. With RPN, you would write 3 6 * 5 +.

While HP calculators were the most common place to encounter RPN, it wasn’t the only place. Friden calculators had it, too. Some early computers and calculators supported it but didn’t name it. Some Soviet-era calculators used it, too, including the famous Elektronika B3-34, which was featured in a science fiction story in a Soviet magazine aimed at young people in 1985. The story set problems that had to be worked on the calculator.

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Closeup of an Apple ][ terminal program. The background is blue and the text white. The prompt says, "how are you today?" and the ChatGPT response says, "As an AI language model, I don't have feelings, but I am functioning optimally. Thank you for asking. How may I assist you?"

Apple II – Now With ChatGPT

Hackers are finding no shortage of new things to teach old retrocomputers, and [Evan Michael] has taught his Apple II how to communicate with ChatGPT.

Written in Python, iiAI lets an Apple II access everyone’s favorite large language model (LLM) through the terminal. The program lives on a more modern computer and is accessed over a serial connection. OpenAI API credentials are stored in a file invoked by iiAI when you launch it by typing python3 openai_apple.py. The program should work on any device that supports TTY serial, but so far testing has only happened on [Michael]’s Apple IIGS.

For a really clean setup, you might try running iiAI internally on an Apple II Pi. ChatGPT has also found its way onto Commodore 64 and MS-DOS, and look here if you’d like some more info on how these AI chat bots work anyway.

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What Makes Wedge Coils Better Than Round For PCB Motors?

PCB motors are useful things. With coils printed right on the board, you don’t need to worry about fussy winding jobs, and it’s possible to make very compact, self contained motors. [atomic14] has been doing some work in this area, and decided to explore why wedge coils perform better than round coils in PCB motor designs.

[atomic14]’s designs use four-layer PCBs which allow for more magnetic strength out of the coils made with traces. While they’ve tried a variety of designs, like most in this area, they used wedge-shaped coils to get the most torque out of their motors. As the video explains, the wedge layout allows a much greater packing efficiency, allowing the construction of coils with more turns in the same space. However, diving deeper, [atomic14] also uses Python code to simulate the field generated by the different-shaped coils. Most notably, it shows that the wedge design provides a significant increase in field strength in the relevant direction to make torque, which scales positively on motors with higher numbers of coils.

This kind of simulation and optimization is typical in industry. It’s great to see an explainer on real engineering methods on YouTube for everyone to enjoy. Video after the break.

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Self-Driving Library For Python

Fully autonomous vehicles seem to perennially be just a few years away, sort of like the automotive equivalent of fusion power. But just because robotic vehicles haven’t made much progress on our roadways doesn’t mean we can’t play with the technology at the hobbyist level. You can embark on your own experimentation right now with this open source self-driving Python library.

Granted, this is a library built for much smaller vehicles, but it’s still quite full-featured. Known as Donkey Car, it’s mostly intended for what would otherwise be remote-controlled cars or robotics platforms. The library is built to be as minimalist as possible with modularity as a design principle, and includes the ability to self-drive with computer vision using machine-learning algorithms. It is capable of logging sensor data and interfacing with various controllers as well, either physical devices or through something like a browser.

To build a complete platform costs around $250 in parts, but most things needed for a Donkey Car compatible build are easily sourced and it won’t be too long before your own RC vehicle has more “full self-driving” capabilities than a Tesla, and potentially less risk of having a major security vulnerability as well.

Turning Old Kindles Into AI Powered Picture Frames

While we tend to think of Amazon’s e-paper Kindles as more or less single-purpose devices (which to be fair, is how they’re advertised), there’s actually a full-featured Linux computer running behind that simple interface, just waiting to be put to work. Given how cheap you can get old Kindles on the second hand market, this has always struck us as something of a wasted opportunity.

This is why we love to see projects like Kindlefusion from [Diggedypomme]. It turns the Kindle into a picture frame to show off the latest in machine learning art thanks to Stable Diffusion. Just connect your browser to the web-based control interface running on the Kindle, give it a prompt, and away it goes. There are also functions to recall previously generated images, and if you’re connecting from a mobile device, support for creating images from voice prompts.

You can find cheap older Kindles on eBay.

All you need is a Kindle that can be jailbroken, though technically the software has only been tested against older third and fourth-generation hardware. From there you install a few required packages as listed in the project documentation, including Python 3. Then you just move the Kindlefusion package over either via USB or SSH, and do a little final housekeeping before starting it up and letting it take over the Kindle’s normal UI.

Given the somewhat niche nature of Kindle hacking, we’re particularly glad to see that [Diggedypomme] went through the trouble of explaining the nuances of getting the e-reader ready to run your own code. While it’s not difficult to do, there are plenty of pitfalls if you’ve never done it before, so a concise guide is a nice thing to have. Unfortunately, it seems like Amazon has recently gone on the offensive, with firmware updates blocking the exploits the community was using for jailbreaking on all but the older models that are no longer officially supported.

While it’s a shame you can’t just pick up a new Kindle and start hacking (at least, for now), there are still millions of older devices floating around that could be put to good use. Hopefully, projects like this can help inspire others to pick one up and start experimenting with what’s possible.

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ChatGPT Makes A 3D Model: The Secret Ingredient? Much Patience

ChatGPT is an AI large language model (LLM) which specializes in conversation. While using it, [Gil Meiri] discovered that one way to create models in FreeCAD is with Python scripting, and ChatGPT could be encouraged to create a 3D model of a plane in FreeCAD by expressing the model as a script. The result is just a basic plane shape, and it certainly took a lot of guidance on [Gil]’s part to make it happen, but it’s not bad for a tool that can’t see what it is doing.

The first step was getting ChatGPT to create code for a 10 mm cube, and plug that in FreeCAD to see the results. After that basic workflow was shown to work, [Gil] asked it to create a simple airplane shape. The resulting code had objects for wing, fuselage, and tail, but that’s about all that could be said because the result was almost — but not quite — completely unlike a plane. Not an encouraging start, but at least the basic building blocks were there. Continue reading “ChatGPT Makes A 3D Model: The Secret Ingredient? Much Patience”