Now:
An unprecedentedly capable and affordable robotic arm for developers and early adopters.
Whats Next:
Modular platform—both hardware and software—designed to integrate seamlessly with the AI models of tomorrow.
A product that can evolve and develop over time, utilizing the model of open hardware.
As AI evolves and as your needs change, you can:
Print your own custom end-effectors for specific tasks.
Swap in more powerful motors for increased payload.
Upgrade to aluminum, Carbon Fiber and etc parts for greater rigidity and precision.
Extend arms and joints to change its reach and form factor.
Ybot is a foundation. It's a platform limited only by your imagination, designed to be the physical body for what next artificial intelligence has to offer
Like the childhood dreams of many, ever since I saw the Iron Man movie, I have not for one second stopped wanting a Jarvis for myself. I was captivated by the scenes in Iron Man's basement workshop—the mechanical beauty of the robotic arms and how they moved.
In 2023, this dream felt distant. The market was polarized: there were ultra-precise industrial arms costing thousands, or simple, low-precision hobbyist kits that were honestly painful for me to look at. There was no middle ground.
So, I decided to build one. I wanted to design an open-source robotic arm that delivered high repeatability and precision at a fraction of the cost, making advanced robotics accessible to more developers, creators, and broke students like myself. This was also a chance I gave myself to learn as I built, allowing me to acquire the skills in the robotics field , the filed that I am passionate about.
Summer of 2025, I called pause on the project.
I wanted to backtrack, give myself a chance to look over the project again, to rethink whats next.
The explosive advancement of AI and Large Language Models in 2025 wasn't just news; it was a paradigm shift. Whether that is the slow shift we see from conventional mathematical based motion planning to AI based maschine learning path planning. Or the rapid rise and developement of LLM and we all of sudden realize that we are on a fasttrack thats taking us closer and closer toward General Intelligence. The dream of a true, contextual, and helpful home AI—a Jarvis—suddenly felt within reach.
As AI is evolving and becoming more accessible, I realized the hardware isn't ready. Future AI needs a physical form that is not only capable and affordable today but also evolvable. I want to create a physical platform that would never become a blockade to software's potential. I envisioned a robot that evolves with its user—where as the AI learns, the hardware can be upgraded, modified, and expanded to keep pace.
So the next generation of Ybot would have to be truly flexible, modular, and built not as a final product, but as a foundation for the future. With that I started the planning and research phase of the next generation:
N1 -->
Designed CAD, selected motor/reducer combos, built the physical structure, and developed C++ ROS2 nodes to translate MoveIt path plans into serial commands for chained servos. Learned URDF export quirks, coordinate transforms between Fusion360 and ROS, and the tradeoffs of serial-bus vs. multi-pin control.
The estimated cost excluding Labour and electricity to produce one unit of the robot arm is $610, and even with those included the eventual retail price should not exceed $1000. Which is very attractive considering how the prices for robots that can achieve my planned accuracy of YBOT, easily esceeds the tens of thousands
From this point onward, I was at the coding phase of the project— which is by far the most challenging part for me, as it's an area I'm least familiar with. I encountered several major obstacles during this stage:
Compatibility and Reliability Issues
One of the first issues I faced was the conpatibility issues between ROS2 and the newest Ubuntu version, and the lack of documentation and community threads for the relativly new ROS2 also made solve this problem more difficult. I ultimately had to rely on trial and error to resolve this. The solutionn was to downgrade to an older Ubuntu release to ensure more reliable performance.
MoveIt Path Planning
Setting up the MoveIt path planning plugin was another major challenge. The main challenge came from the need to export the CAD model of the arm into a URDF file—a format not natively supported by Fusion 360. So I had to use a third-party plugin to complete the export. This causes several very confusing problems that i had to solve, including a weird orientation issues where the exported models would always be laying on its side, which i later discovered to be due to the different XYZ coordinate system Fusion and ROS uses.
Hardware Interface and Intergration
My original plan was to use the 5 pins on the servo motor controller, each of which would control a component of the motors motion (Eg. Speed, Angle, Acceleration ...). For a total of six motors this would need 30 pins that can individually output changing voltage. However, midway through construction, I realized that since the off-the-shelf components I bought actually supported an alternative communication method called Serial Bus. This discovery allowed me to not use the 5 pins but only 2 pins and 2 wires total, as the wire would chain together all the motors.
For the hardware interface, I learned C++, and used it to code a node that would listen for the planned path and convert it into serial signals the motors can understand. And surprisingly it actually worked, however...
However, it was during this phase that I discovered a major design flaw: I had completely overlooked the need for angle sensors or limiters. This meant that there is no way for the robot to know calibrate its joint positions, which means the arm's movements lack any real precision or accuracy. Although it appeared to be moving correctly, there are no sensors that I can use to calibrate its simluation position. This was not all however, as I continued testing, I identified several additional mechanical design flaws:
Glue Belts – These were often either over-tensioned (causing them to snap under load) or under-tensioned (leading to slippage).
Wobbly Axles – The axles connecting the motor and speed reducer were only supported on one side, resulting in wobbling during motion. This not only reduced tension consistency but also applied undue stress on the reducers.
Poor Cable Management – The robot arm couldn't rotate 180 degrees without cables tangling. Additionally, the cables weren't properly secured to the arm’s structure.
these combined issues together made me decide to halt the further development of the software, and go back to more research to construct a more solid foundation.
Main Progress:
Finally Designed the Base
Designed With Cable Management in mind
Refined and determined design details
Optimized Weight
Main lessons learned:
ALWAYS, ALWAYS consider wire managment when designing, and before modeling.
Main Progress:
Determined the specific model of motor and reducer
Considered the arrangement of components and detailed dimensions of the limbs
Determined the design style of the robot
Main lessons learned:
Always have a holistic view when designing (model, components, wiring)
Still don’t know how I will design the base joint
Main Progress:
Defined the general shape and geometry of the joints
Converted into a 3D scale
Lessons learned:
For the compactness of the arm, and ensure 360 degrees of movement, the third joint should take a L shape,
The base of the Robot is extremely difficult to make