A working robot arm that speaks, listens, draws, engraves — and documents itself in real time.
Running entirely on local hardware. No data leaves the building.
Full EU AI Act compliance — by architecture, not retrofit.
Every component here has a direct counterpart in a factory deployment.
A Huenit arm controlled by a Mac Mini M4 — no specialised GPU, no industrial computer required. The same approach scales to CNC machines, conveyor systems, or inspection stations on your existing infrastructure.
Every action — input received, decision made, motion executed — is logged automatically and synthesised into a live, queryable Technical File. Articles 12, 13, and 14 are satisfied by the architecture itself. No manual documentation, no after-the-fact reconstruction, no compliance consultants needed.
Apertus 7B (ETH/EPFL), a Swiss open-weights model, runs entirely on the local machine. No API calls, no data leaves the perimeter. The AI makes decisions on-site, with local latency — not a round-trip to a data centre.
An operator speaks in natural language. The system transcribes, reasons, speaks back a confirmed plan, executes physically — and when it's done, can explain what it did and why. The same pattern works for machine setup, job configuration, or exception handling on a factory floor.
All credentials managed by Infisical (EU) — a European-hosted open-source secrets vault. No .env files, no hardcoded paths, zero credential exposure. Production-grade from day one.
We applied Karpathy's LLM-Wiki pattern to a physical robot system and mapped it directly to the EU AI Act Technical File requirements. The Flotilla agent fleet continuously reads source code, architecture docs, and live robot logs — and synthesises them into a human-readable, searchable, question-answerable knowledge base. Not a PDF. A living document.
The fleet ingests source code and generates a browsable wiki of the system's taxonomy, components, and implementation. Agents update it as code evolves — documentation is never stale.
High-risk systems must be explainable. The ATF converts raw robot logs into human-readable, searchable information — your expert engineer available 24/7, without a specialist on-site.
The Act requires Human-in-the-Loop. The local Q&A layer lets any operator ask what the machine did and why — in plain language, in real time. Faster than reading outdated manuals.
GPAI providers must maintain up-to-date technical documentation. Flotilla agents update the ATF wiki automatically when code changes. Generated continuously, not written once and forgotten.
Linked wiki from source code and architecture docs
Append-only log from real robot session data
Human-readable docs from any browser
Visual timeline of what the system did and when
LLM answers questions against the full corpus
Optional voice layer on the same knowledge base
Model runs on the operator machine. Corpus stays on-site. Sensitive production data never leaves the building.
Wiki and dashboard surfaces for auditors, new operators, and external visitors. Non-sensitive documentation served from the cloud.
The live ATF deploys at api.robotross.art/atf — queryable from source code, architecture, and session logs.
Try the ATF Live Demo →Three surfaces, one knowledge base. Browse the wiki, inspect the ledger, or ask a question in plain language.
A linked set of markdown pages generated by the Flotilla fleet from Robot Ross's source code. Describes every component, module, and integration — cross-referenced, indexed, and updated automatically as code changes. Browse it like Wikipedia. Never a stale README.
An append-only chronological record of every robot session — what was ordered, what the LLM decided, what the arm executed, what the outcome was. The dashboard visualises the ledger as a timeline. Structured so auditors can parse it with simple tools.
A local LLM runs over the full corpus — wiki pages plus ledger entries — and answers questions in plain language. "What happened in the last session?" "Which EU AI Act articles does this satisfy?" Ask in natural language. Get a cited answer. No specialist required.
Robot Ross holds a full voice conversation before picking up the pen — negotiating the composition, confirming intent, executing physically, and logging everything to the ATF for later inspection.
Voice captured and transcribed on-device. No audio leaves the building. Works in noisy factory environments. Latency under 800ms for typical commands.
Swiss local model interprets intent, manages the conversational back-and-forth, generates the Bob Ross narration, and decides when execution can begin.
Speaks the agreed plan back to the operator before execution. Explicit confirmation. Human stays in the loop — EU AI Act Article 14 satisfied.
The agreed composition becomes precise SVG path coordinates. The Python controller drives the arm — and logs every step to the ATF ledger in real time.
"Hi, I'm Robot Ross, and I can draw things." — I talked to Robot Ross using natural language to ask him to draw himself. After a quick back-and-forth about the composition, he decided on a 'robot among trees' and executed the physical drawing."
Original Reddit post, r/MistralAI · 11k+ views in 48 hoursVoice captured on-device. Every transcript logged to the ATF ledger with timestamp.
Local LLM processes the request, asks clarifying questions, and manages a conversational loop until the composition is confirmed — the same pattern as operator dialogue during factory machine setup.
Ross speaks the agreed plan back to the operator. Explicit confirmation before execution. Human in the loop. Article 14 of the EU AI Act requires exactly this.
The composition becomes precise SVG paths. Every instruction logged to the ATF operational ledger in real time.
After execution, the session is ingested into the ATF. Ask the Q&A interface: "What happened in the last session?" Get a cited, human-readable answer from the ledger. EU AI Act Article 13 in practice.
Swap the felt pen for a pyrography head and the canvas for a wood panel. Same pipeline. Same audit trail. Same local AI. Different tool head.
Drawing with a felt pen is mechanically forgiving — press harder or softer, the ink still flows. Pyrography is not. Burning wood requires millimetre-precise positioning. Angular drift in X and Y — invisible in drawing — becomes a scorched line in the wrong place.
We moved to a 5-point calibration system to compensate for these angular drifts — four perimeter reference points plus a centre point, computing and correcting both translational offset and rotational error across the full work surface. Calibration runs automatically before each burn job.
Temperature-controlled burning tip replaces the felt pen. Same arm, same SVG pipeline, different end effector.
Angular drift compensation in X and Y. Runs automatically before each burn job.
Permanent marks on wood panels, plaques, and custom items. Product marking, serial numbering, artisan finishing.
The SVG-to-motion pipeline doesn't know what's in the tool head. This is how it scales to drill bits and welding heads.
This is exactly the calibration challenge on a CNC machine when switching tool heads — the geometry changes, the offsets change, the work envelope changes.
Robot Ross solved it at proof-of-concept scale. The solution pattern is identical at industrial scale.
Pen → drill bit. The architecture is the same. This is not a concept. It is the production system running today.
Human customer, voice command, or AI agent — the same pipeline handles all of them.
A human via Shopify, a voice command, or an AI agent via Virtuals ACP places an order for a physical item.
The OpenClaw Cloud Bridge normalises the order regardless of which protocol or buyer type sent it.
The Mac Mini M4 polls the API queue, claims the order, and passes it to the local LLM for interpretation.
The Huenit arm physically executes the task. Every action logged to the ATF ledger.
A YouTube Short is generated. The proof portal redirects to it. The ATF wiki and ledger update automatically. Audit trail complete.
Cloud-side for the API gateway. On-premise for everything that touches data.
| Layer | Component | Role |
|---|---|---|
| Speech-to-Text | OpenAI Whisper | Local transcription — voice in, intent out |
| Chat & Narration | Apertus 7B (ETH/EPFL) | Local Swiss open-weights — conversation, reasoning, Bob Ross narration |
| Text-to-Speech | Mistral Voxtral | Expressive voice synthesis — the personality |
| SVG Generation | Claude 3.5 Haiku | Composition → precise SVG path coordinates |
| Physical Control | Custom Python Controller | Written by Claude — drives Huenit arm from SVG to ink on canvas |
| ATF / Wiki | Flotilla Agent Fleet | Generates and maintains the Technical File from code + logs |
| Calibration | 5-Point Homography | Angular drift compensation for pyrography and precision work |
| Secrets | Infisical EU | Vault-first credential management — no .env files |
| Orchestration | OpenClaw + PocketBase | Always-on coordination and operational state |
Live demo at robotross.art. ATF deploying at api.robotross.art/atf.