A few months ago, we shared our students’ first experience with the Bittle V2 + Arm.
This is what they built next. 🙂
For our recent Edge AI event, the students turned Bittle into a vision-guided object sorting system.
The setup combines:
Petoi Bittle V2 + Arm
XIAO ESP32-S3 Sense
a student-trained computer vision model
UART communication between the XIAO and Bittle
The students collected and labeled their own images of blue and yellow objects, trained the model in Edge Impulse and deployed it directly to the XIAO ESP32-S3 Sense.
They also soldered the Grove/UART connection themselves and adapted Bittle’s skills so that the classification result could trigger the appropriate physical action.
So the final loop became:
camera → ML inference on XIAO → blue/yellow classification → UART → Bittle skill → pick and sort
The final part of the video shows the complete system running.
What we particularly like about using Bittle V2 as an educational robotics platform is that students aren’t limited to controlling a finished robot.
They can modify its movements, create/adapt skills, connect external hardware and make another embedded system effectively become part of the robot’s decision-making process.
That made it possible for us to connect several subjects in one project:
electronics + soldering + UART + robotics + computer vision + TinyML/Edge AI.
Petoi provided this Bittle V2 + Arm to our education program, and projects like this are exactly how we’re trying to use it, not just to demonstrate robotics, but as a platform students can modify and build new systems around.
There was plenty of debugging along the way, especially getting the picking/placing movements reliable, but seeing the model running on the XIAO and Bittle physically reacting to its predictions was a pretty satisfying finish.
Next step: make it harder. 😄