Reachy The Open Source Robot Says Bonjour

Humanoid robots always attract attention, but anyone who tries to build one quickly learns respect for a form factor we take for granted because we were born with it. Pollen Robotics wants to help move the field forward with Reachy: a robot platform available both as a product and as a wealth of information shared online.

This French team has released open source robots before. We’ve looked at their Poppy robot and see a strong family resemblance with Reachy. Poppy was a very ambitious design with both arms and legs, but it could only ever walk with assistance. In contrast Reachy focuses on just the upper body. One of the most interesting innovations is found in Reachy’s neck, a cleverly designed 3 DOF mechanism they called Orbita. Combined with two moving antennae at the top of the head, Reachy can emote a wide range of expressions despite not having much of a face. The remainder of Reachy’s joints are articulated with Dynamixel serial bus servos though we see an optional Orbita-based hand attachment in the demo video (embedded below).

Reachy’s € 19,990 price tag may be affordable relative to industrial robots, but it’s pretty steep for the home hacker. No need to fret, those of us with smaller bank accounts can still join the fun because Pollen Robotics has open sourced a lot of Reachy details. Digging into this information, we see Reachy has a Google Coral for accelerating TensorFlow and a Raspberry Pi 4 for general computation. Mechanical designs are released via web-based Onshape CAD. Reachy’s software suite on GitHub is primarily focused on Python, which allows us to experiment within a Jupyter notebook. Simulation can be done within Unity 3D game engine, which can be optionally compiled to run in a browser like the simulation playground. But academic robotics researchers are not excluded from the fun, as ROS1 integration is also available though ROS2 support is still on the to-do list.

Reachy might not be as sophisticated as some humanoid designs we’ve seen, and without a lower body there’s no way for it to dance. But we are very appreciative of a company willing to share knowledge with the world. May it spark new ideas for the future.

[via Engadget]

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Still Got Film To Scan? This Lego And Raspberry Pi Scanner Is For You

There was a time during the early years of mass digital photography, when a film scanner was a common sight. A small box usually connected to a USB port, it had a slot for slides or negatives. In 2020 they’re  a rare breed, but never fear! [Bezineb5] has a solution in the shape of an automated scanner using a Radpberry Pi and a mechanism made of Lego.

The Lego mechanism is a sprocket feeder that moves the film past the field of view from an SLR camera. The software on the Pi runs in a Docker container, and features a machine learning approach to spotting frame boundaries. This is beyond the capabilities of the Pi, so is offloaded to a Google Coral accelerator.

The whole process is automated with the Pi controlling not only the Lego but also the camera, to the extent of retrieving the photos from it to the Pi. There’s a smart web interface to control everything, making the process — if you’ll excuse the pun — a snap. There’s a video of it in action, that you can see below the break.

We’ve featured many film scanner projects over the years, one that remains memorable is this 3D printed lens mount.

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Google Launches AI Platform That Looks Remarkably Like A Raspberry Pi

Google has promised us new hardware products for machine learning at the edge, and now it’s finally out. The thing you’re going to take away from this is that Google built a Raspberry Pi with machine learning. This is Google’s Coral, with an Edge TPU platform, a custom-made ASIC that is designed to run machine learning algorithms ‘at the edge’. Here is the link to the board that looks like a Raspberry Pi.

This new hardware was launched ahead of the TensorFlow Dev Summit, revolving around machine learning and ‘AI’ in embedded applications, specifically power- and computationally-limited environments. This is ‘the edge’ in marketing speak, and already we’ve seen a few products designed from the ground up to run ML algorithms and inference in embedded applications. There are RISC-V microcontrollers with machine learning accelerators available now, and Nvidia has been working on this for years. Now Google is throwing their hat into the ring with a custom-designed ASIC that accelerates TensorFlow. It just so happens that the board looks like a Raspberry Pi.

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