Do you remember the movie WALL-E? Apparently, [Leviathan engineering] did, and he wasn’t as struck by the title character, or Eva, or even the Captain. He was captivated by BURN-E. His working model shows up in the video below.
We’ll be honest. BURN-E didn’t ring a bell for us, though we remember the movie. He grabbed a 3D design for the robot on the Internet and planned out holes for some servos and other hardware.
As the old saying goes, when the only tool you have is a 6 DOF industrial robotic arm, every problem looks like an opportunity to make it serve up adult beverages. [benkokes] found himself in this familiar predicament and did what any of us would do, but his process wasn’t without a few party fouls as well as a few head-scratchers.
One of the common problems that people who suddenly find themselves with an old industrial robot have is that there’s usually no documentation or instructions. This was true here with the added hiccup of the robot’s UI being set to Chinese. Luckily no one had changed the root password, and eventually he was able to get the robot up and working.
Getting it to make drinks was a different matter altogether. [benkokes] needed a custom tool to hold the cup as well as shake it, and 3D printed a claw-style end effector with a lid. Out of his multi-colored pack of party cups, however, the orange cups were different enough in dimension to cause problems for the shaking lid which was discovered when the robot spilled a drink all over the table.
Eventually, though, the robot was successfully serving drinks at a party. One of [benkokes]’s friends happened to be a puppet maker and was able to outfit it with a tailored tuxedo for the party as well, and he also programmed it to dance in between serving drinks, completing the AI revolution we have all been hoping for. Perhaps unsurprisingly, this is a common project for people who suddenly come to posses a large general-purpose industrial robot, while others build robots specifically for this task alone.
Imagine trying to balance a heavy metal ball bearing on a cafeteria tray. It’s not the easiest thing in the world! In fact, it’s perhaps a task better automated, as [skulkami3000] demonstrates with this robotic build.
The heart of the build is a flat platform fitted with a resistive touchscreen panel on top. The panel is hooked up to a Teensy 4.0 microcontroller. When a heavy ball bearing is placed on the touch panel, the Teensy is thus able to accurately read its position. It then controls a pair of NEMA 17 stepper motors via TCM2208 drivers in order to tilt the panel in two axes in order to keep the ball in the centre of the panel. Thanks to its quick reactions and accurate sensing, it does a fine job of keeping the ball centred, even when the system is perturbed.
Projects like these are a great way to learn the basics of PID control. Understanding these concepts will serve you well in all sorts of engineering contexts, from controlling industrial processes to building capable quadcopter aircraft. Continue reading “Robot Balances Ball On A Plate”
Many decades ago, IBM engineers developed the typeball. This semi-spherical hunk of metal would become the heart of the Selectric typewriter line. [James Brown] has now leveraged that very concept to create a pivoting mouth mechanism for a robot that appears to talk.
What you’re looking at is a plastic ball with lots of different mouth shapes on it. By pivoting the ball to different angles inside the head of a robot, it’s possible to display different mouth shapes on the face. By swapping mouth shapes rapidly in concert with recorded speech, it’s possible to make the robot appear to be speaking. We don’t get a great look at the mechanism that operates the ball, but Selectric typeball operation is well documented elsewhere if you seek to recreate the idea yourself.
The real benefit of this mechanism is speed. It might not look as fluid as some robots with manually-articulated flexible mouths, but the rapid mouth transitions really help sell the effect because they match the pace of speech. [James] demonstrated the finished product on Mastodon, and it looks great in action.
This isn’t the first time we’ve featured [James Brown]’s work. You may recall he got DOOM running on a tiny LEGO brick a few years back.
Thanks to [J. Peterson] for the tip!
Reachy Mini is a kit for a compact, open-source robot designed explicitly for AI experimentation and human interaction. The kit is available from Hugging Face, which is itself a repository and hosting service for machine learning models. Reachy seems to be one of their efforts at branching out from pure software.
Reachy Mini is intended as a development platform, allowing people to make and share models for different behaviors, hence the Hugging Face integration to make that easier. On the inside of the full version is a Raspberry Pi, and we suspect some form of Stewart Platform is responsible for the movement of the head. There’s also a cheaper (299 USD) “lite” version intended for tethered use, and a planned simulator to allow development and testing without access to a physical Reachy at all.
Reachy has a distinctive head and face, so if you’re thinking it looks familiar that’s probably because we first covered Reachy the humanoid robot as a project from Pollen Robotics (Hugging Face acquired Pollen Robotics in April 2025.)
The idea behind the smaller Reachy Mini seems to be to provide a platform to experiment with expressive human communication via cameras and audio, rather than to be the kind of robot that moves around and manipulates objects.
It’s still early in the project, so if you want to know more you can find a bit more information about Reachy Mini at Pollen’s site and you can see Reachy Mini move in a short video, embedded just below.
Continue reading “Reachy The Robot Gets A Mini (Kit) Version”
Having a robot that can quickly and unsupervised pick any lock with the skills of a professional human lockpicker has been a dream for many years. A major issue with lockpicking robots is however the lack of any sensing of the pins – or equivalent – as the pick works its magic inside. One approach to try and solve this was attempted by the [Sparks and Code] channel on YouTube, who built a robot that uses thin wires in a hollow key, load cells and servos to imitate the experience of a human lockpicker working their way through a pin-tumbler style lock.
Although the experience was mostly a frustrating series of setbacks and failures, it does show an interesting approach to sensing the resistance from the pin stack in each channel. The goal with picking a pin-tumbler lock is to determine when the pin is bound where it can rotate, and to sense any false gates from security pins that may also be in the pin stack. This is not an easy puzzle to solve, and is probably why most lockpicking robots end up just brute-forcing all possible combinations.
Perhaps that using a more traditional turner and pick style approach here – with one or more loadcells on the pick and turner- or a design inspired by the very effective Lishi decoding tools would be more effective here. Regardless, the idea of making lockpicking robots more sensitive is a good one, albeit a tough nut to crack. The jobs of YouTube-based lockpicking enthusiasts are still safe from the robots, for now.
Continue reading “A Lockpicking Robot That Can Sense The Pins”
Hold onto your hats, everyone — there’s stunning news afoot. It’s hard to believe, but it looks like over-reliance on chatbots to do your homework can turn your brain into pudding. At least that seems to be the conclusion of a preprint paper out of the MIT Media Lab, which looked at 54 adults between the ages of 18 and 39, who were tasked with writing a series of essays. They divided participants into three groups — one that used ChatGPT to help write the essays, one that was limited to using only Google search, and one that had to do everything the old-fashioned way. They recorded the brain activity of writers using EEG, in order to get an idea of brain engagement with the task. The brain-only group had the greatest engagement, which stayed consistently high throughout the series, while the ChatGPT group had the least. More alarmingly, the engagement for the chatbot group went down even further with each essay written. The ChatGPT group produced essays that were very similar between writers and were judged “soulless” by two English teachers. Go figure.
