A robotics maker built a functional laundry-folding robot prototype, Sourccey, in under 24 hours for a client prioritizing functionality over aesthetics. The robot utilizes 3D-printed components for rapid prototyping and part replacement, highlighting the speed of development in robotics. This follows the development of another robot using salvaged parts last year.
A robotics maker has showcased a functional laundry-folding robot prototype made in under 24 hours. Built for a client focused on results rather than polish by Nick Maselli, the robot—named Sourccey—features a cylindrical, mobile body with a domed top, two articulated arms, and a central vertical lift for handling garments.
Most structural components were 3D-printed in Polylactic Acid , enabling fast iteration and part replacement throughout the build, highlighting the flexibility of rapid prototyping in robotics development.Last year, another robotics enthusiast built Esghati, a browser-controlled robot from salvaged parts, featuring built-in Wi-Fi, live camera access, and face recognition.Rapid robotics buildMaselli has demonstrated how quickly functional robotics systems can be assembled by building a laundry-folding robot prototype, named Sourccey, in less than 24 hours. The project was commissioned by a client with a clear requirement: automate laundry folding as fast as possible, with performance taking priority over aesthetics.Sourccey is built around a mobile cylindrical chassis topped with a dome-shaped enclosure. The robot is equipped with two articulated arms mounted to a central vertical lift mechanism. This Z-axis actuator runs through the center of the body, allowing the arms to raise and lower to interact with laundry placed either on the floor or on a tabletop. The mechanical layout provides sufficient reach and flexibility to manipulate soft materials, which is significantly more challenging than handling rigid objects, reports Techeblog.Most of the robot’s structural components were produced using 3D printing with PLA filament. These parts include the arm segments, mounting brackets, and exterior panels. Rapid printing enabled fast iteration during the tight build window, allowing damaged or suboptimal components to be reprinted and swapped out within hours. This approach proved critical when addressing issues such as defective printed parts encountered during assembly.Each arm incorporates multiple servo motors arranged to provide precise, coordinated movement. Accurate alignment of these motors is essential to ensure synchronized gripper motion when folding fabric. The grippers at the end of each arm are designed to maintain consistent tension on cloth, preventing slippage during manipulation. Electrical safety and reliability were addressed through careful wiring, the use of fuses, and a structured power distribution system, reports Hackster.Machine-learned foldingAt the core of the system is a Raspberry Pi 5 single-board computer, which serves as the robot’s primary controller. Despite its compact form factor, the Pi handles a substantial workload. It processes input from four camera feeds used for vision-based perception, controls motor drivers for the arms and lift system, and operates peripheral hardware including a display, speakers, and a microphone. The Raspberry Pi also runs the software stack responsible for task execution and system coordination.Power is supplied by a 12-volt, 10-amp-hour lithium iron phosphate battery, selected for its thermal stability, safety characteristics, and long cycle life. A custom-designed power distribution printed circuit board manages power flow throughout the system. Integrated voltage converters ensure that each component receives the correct operating voltage without overload, particularly the servo motors, which can draw high current during operation.Instead of relying on hard-coded motion scripts, Sourccey uses a learning-based approach. A human operator first demonstrates the folding process through a teleoperation system, generating training data from real movements. This data is then used to train an AI model overnight on high-performance GPUs. Once trained, the model is deployed back to the Raspberry Pi, where it runs locally, reports Techeblog. Using its multi-camera vision system, the robot identifies laundry items, determines their orientation and shape, and executes folding motions learned during training. Because the system relies on visual feedback and learned behavior rather than fixed paths, it can adapt to the natural variability and flexibility of fabric. Maselli also designed the software architecture to allow new AI models to be uploaded, enabling the robot to potentially learn additional household tasks beyond laundry folding, reports Hackster. The entire hardware assembly was completed during the day, with AI training performed overnight. Despite minor setbacks—including a missing motor clip and the need to reprint faulty components—the prototype completed its task, highlighting the effectiveness of rapid prototyping combined with modern embedded computing and machine learning.
Robotics Laundry Robot 3D Printing Rapid Prototyping Automation
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