Teaching Stack-chan to Drive Autonomously with LEGO Wheels and Open-Source Robium Skills
Stack-chan gets LEGO wheels and learns to self-drive via ACT imitation learning — no control logic written by hand.
A developer transformed M5Stack's desktop mascot Stack-chan into a wheeled robot, recording human driving demonstrations and training an ACT (Action Chunking Transformer) policy to achieve autonomous line-following and guardrail navigation without any hand-written control logic. The project serves as the first showcase for Robium, an open-source robotics skills repository, with AI agents assisting in software and training setup throughout. Its value lies in running a complete data collection–training–deployment loop on minimal hardware, proving imitation learning is viable on ultra-low-cost platforms.
From Desktop Mascot to Self-Driving Robot
In the robotics hobbyist community, the M5Stack Stack-chan has always been a popular little gadget — essentially a desktop mascot with an expressive screen. But one developer took it in a completely new direction: attaching LEGO motors and wheels, then teaching it to drive itself.
What makes this project interesting isn't the complexity of the hardware, but the fact that it demonstrates one of the hottest techniques in robotics today — Learning from Demonstrations (LfD) — with an extremely low barrier to entry. Instead of writing a single line of control logic by hand, the developer recorded human driving demonstrations, trained a policy network, and let the robot learn to follow a line and navigate between guardrails on its own.
Replacing Hand-Written Control Logic with Imitation Learning
Traditional line-following robots typically rely on rule-based programming: read sensors, measure deviation, adjust motors. This project instead uses an ACT (Action Chunking Transformer) policy to train the robot's behavior.
The process breaks down into three steps:
1. Build the Hardware
The developer combined Stack-chan with a LEGO motor hub and wheels, transforming a display-only device into a mobile wheeled platform. This "snap-together" approach lowers hardware costs and makes the project easier to reproduce.
2. Collect Demonstration Data
By manually driving the robot through its tasks, the system records the demonstration data. Notably, line-following and guardrail navigation each used a separate demonstration dataset — meaning different tasks were trained from different demonstrations rather than a single universal ruleset.
3. Train the ACT Policy
An ACT policy model is trained on the collected data. In supervised experiments, the robot successfully learned to follow a line; based on a separate set of demonstrations, it also attempted to drive between guardrails.
The developer candidly acknowledged plenty of "going the wrong way" moments, and the video honestly shows those failure clips too. This authenticity reflects the reality of deploying imitation learning in practice — data quality and demonstration consistency directly determine how well the policy performs.
Background: Imitation Learning and ACT
Learning from Demonstrations (LfD) is a paradigm in robot learning: rather than manually designing control rules, an agent learns behavioral strategies by observing human demonstrations. The core assumption is that "good behavior is already embedded in the demonstration data," and the training objective is to make the policy network's outputs closely match the demonstrator's actions.
ACT (Action Chunking with Transformers) is a concrete implementation proposed by Stanford University in 2023. Its key innovation is "action chunking" — packaging multiple consecutive action steps into a single "action chunk" for unified prediction, rather than outputting one action per frame. This effectively reduces compounding errors and makes the policy more robust to temporal variations. ACT uses a Transformer architecture to process visual and proprioceptive inputs, originally achieving excellent results on dexterous manipulation tasks with robot arms. It is now being ported to lower-cost platforms to test its generalization capabilities.
Robium: An Open-Source Robotics Skills Repo Makes Its Debut
This small robot project is actually the first demonstration the developer built to showcase their newly released open-source project, Robium.
Robium is an open-source robotics skills repository aimed at making common robot capabilities modular and reusable. In this project, the developer also used AI agents to assist with building the software and training pipeline — meaning AI tools were involved from code writing to setting up the training environment.
This reflects an emerging trend: robot development is shifting from "write everything from scratch" toward "call skill modules + AI-assisted orchestration." For individual developers and small teams, this model can dramatically compress the time from idea to working prototype. The project code is open-sourced on GitHub at robium-ai/robium for anyone interested in exploring or reproducing it.
Background: The Robotics Skills Repository Concept
The concept of a "robotics skills repository" borrows from the package management philosophy in software engineering: encapsulating common robot capabilities — grasping, line-following, obstacle avoidance — into standalone, reusable modules. Developers build complex behaviors by calling and composing these modules, without re-implementing low-level logic each time. This approach shares similarities with ROS (Robot Operating System)'s node-based design, but focuses more on abstracting high-level semantic skills rather than low-level communication middleware.
Combined with large language models or AI agents as an "orchestration layer," the value of a skills repository is further amplified — AI can automatically select and chain skill modules based on task descriptions, translating natural language instructions into executable robot behavior pipelines. Robium is an early attempt in this space, and its open-source nature means community contributions can continuously expand the library's coverage.
Why This Small Project Is Worth Paying Attention To
Beyond the cute exterior, this project offers genuine reference value on several fronts.
Validating cutting-edge methods at low cost. ACT policies and imitation learning typically appear in the context of expensive platforms like robot arms and humanoids. Getting the full data collection → training → deployment loop running on a modified desktop toy is enormously valuable for education and onboarding.
Open-source ecosystem in action. Robium isn't a framework that exists only on paper — it proves its usability through a project you can actually see moving. The combination of open source plus a real working demo is far more convincing than a code repository alone.
A concrete sample of AI-assisted development. The developer explicitly mentioned using AI agents to help build the software and training setup, providing a specific, grounded example of "how AI can accelerate robot development" rather than abstract rhetoric.
Conclusion
From a charming desktop mascot to an autonomously navigating wheeled robot, this project demonstrates the viability of imitation learning in the most approachable way possible. It serves as both the technical debut of the Robium open-source repository and a vivid example of how individual developers can rapidly build robotic systems with the help of AI tools.
The developer has also put an open question to the community: has anyone else done demonstration-based learning on small wheeled robots? What approaches worked? This kind of open exchange is exactly what keeps the open-source robotics ecosystem evolving.
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