A robot arm picking up rubber ducks does not sound like a milestone until you notice that nothing in the loop is talking to a server. Dmitry Maslov of Hardware.ai bolted Arduino's new VENTUNO Q onto an SO-101 arm and let a vision-language-action model do all the thinking on-board, with two cameras and a stack of servos. No cloud inference bill, no latency spike when the WiFi drops.
Two cameras and 50 demonstrations
The arm is the boring part. An SO-101 is a servo at every joint, the kind of kit that turns up in second-year robotics labs and thesis defences. The brain is where the work happened. Maslov fed the board video from an overhead camera plus a second camera on the gripper, added joint-position feedback, and trained Hugging Face's SmolVLA model on roughly 50 demonstrations of the pick-and-place task. Fifty is a tiny dataset, and the arm still finds the ducks. The full build and video are on Hackster.
What is actually on the board
The VENTUNO Q keeps the split-brain layout that made the UNO Q popular: a microcontroller side and a single-board-computer side sharing one PCB. The MDB half runs an STM32H5F5, an Arm Cortex-M33 clocked at 250MHz, and that is the half you care about for servo timing, GPIO, and the pins you will actually solder to. The SBC half carries a Qualcomm Dragonwing IQ8 with a Kryo Gen 6 CPU, an Adreno 623 GPU, and a Hexagon Tensor NPU, plus 16GB of LPDDR5 and 64GB of eMMC.
That NPU is the reason local VLA inference is practical here rather than a slideshow. Arduino prices the board at $299. The nearest thing on the shelf is the Jetson Orin Nano Super Developer Kit at $399, which wins on some specs and loses on others, but does not hand you a Cortex-M33 on the same board.
Build it yourself
You do not need a $299 board to start. The path most students take looks like this:
- Get a 4-6 DOF servo arm moving first. An Arduino Uno plus a PCA9685 driver over I2C (SDA/SCL) is enough, and a separate 5V supply keeps servo current spikes off your logic rail.
- Record joint angles to serial while you drag the arm through a task by hand. That is your demonstration dataset, and it is the part everyone skips.
- Add one USB camera and a simple colour-blob detector before you reach for a neural network. If your gripper cannot close on a fixed target, a VLA model will not save it.
- Move to on-board inference only when the mechanical side is repeatable.
The tricky bit is never the model. It is servo backlash and a gripper that closes half a centimetre short. Fix the arm, then upgrade the brain.
Originally published on blog.circuit.rocks.
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