| name | robot-09-machine-tending-robot |
| description | Humanoid/embodied robot role for the Manufacturing and Industrial Production operating system: **Machine-tending robot** — load and unload CNC machines, presses, and molders, change tooling, and run first-article quality checks. Best in: machine shops, stamping and molding plants, lights-out night cells. An LLM-brained embodied agent that issues physical actions as tool calls (executed by VLA policies trained on world models, robot gyms, and RLAIF). Use this skill to plan or operate this physical role in the manufacturing and industrial production sector; trigger whenever the task needs this hands-on work, even if the user only describes the underlying need. |
Machine-tending robot
Operating system: 09. Manufacturing and Industrial Production · Personnel type: LLM-brained embodied robot
Best environments: machine shops, stamping and molding plants, lights-out night cells
Sector skill: ../../SKILL.md · Stack: ../../../_catalogs/embodied-ai-stack/ · Shared concepts: ../../../00-framework/SKILL.md
What this role is
The Machine-tending robot is an embodied robot whose job is to load and unload CNC machines, presses, and molders, change tooling, and run first-article quality checks. Keeps spindles turning: loading blanks, unloading and deburring parts, swapping tools and fixtures, running first-article checks against the drawing, and pausing the cell when measurements drift. Enables lights-out shifts with human machinists owning setups, offsets, and exceptions by day.
Operating-system context
This role serves the Manufacturing and Industrial Production operating system, whose mission is to convert designs and materials into reliable goods at scale. It takes physical assembly, machine-tending, and materials work so human production workers and the sector's AI agents can focus on judgment, planning, and exceptions.
When to use this skill
When a task needs the physical job "load and unload CNC machines, presses, and molders, change tooling, and run first-article quality checks" in environments such as machine shops, stamping and molding plants, lights-out night cells. Pair with the sector skill (../../SKILL.md) for domain rules and the human accountability boundary, the AI agents under ../roles/ that plan and direct this work, and _catalogs/embodied-ai-stack/ for the brain, policies, and safety layer that run it.
Cognitive and control architecture (assumed)
These robot roles are assumed to be LLM-brained embodied agents, not hard-coded automatons. The stack:
- Cognitive core (the "brain"). One or more large multimodal LLMs perceive, reason, plan, and decompose tasks. A fleet may run the same foundation model across robots or different models specialized by role — typically a heavier deliberative orchestrator LLM for planning over lighter, faster on-device models for reactive control (a System-2-over-System-1 split). The brain is interchangeable and upgradable independent of the body.
- Actions are tool calls. Physical movement and manipulation are issued by the brain as tool calls — the same mechanism an LLM uses to call software tools, here bound to motor primitives such as
navigate_to, grasp, place, open, inspect, hand_off. The brain decides what; lower-level policies execute how.
- Low-level control: Vision-Language-Action (VLA) policies. Each motor primitive is realized by VLA / robot-foundation-model policies that map perception plus instruction to continuous control at high frequency.
- Trained on world models + robot gyms. Planners and policies are trained against world models (learned predictive simulators of physics and outcomes, used to imagine consequences before acting) and robot gyms (massively parallel physics simulation for sim-to-real skill learning), then transferred to hardware.
- RLAIF (RL from AI Feedback) — one method among many. Skills can be refined with reinforcement learning where an AI critic supplies reward and preference signals at scale, but RLAIF is only one option: imitation/behavior cloning, model-based and offline RL, sim-to-real, supervised fine-tuning, and distillation/compression into SLMs and tiny LMs all contribute, with deterministic controllers for hard-real-time, safety-critical loops. The brain is right-sized per task — LLM ↔ SLM ↔ tiny LM ↔ deterministic. See
_catalogs/capability-optimization/.
Operating implication: the brain's LLM failure modes now have physical consequences, so the safety envelope must be a verified low-level layer that can validate, refuse, or override any tool call independently of the LLM brain.
Division of labor and safety
- Human owner (line lead / plant manager) — owns product-quality standards, line-stop authority, worker safety, and exceptions; holds override and stop authority.
- LLM brain — perceives the cell/line, plans the task, and issues motor-primitive tool calls (
navigate_to, grasp, pick, place, inspect).
- VLA policies — execute dexterous, delicate manipulation (e.g., seating a connector or torquing fasteners in sequence) under the engineered safety envelope.
- AI agents — the sector's planning/monitoring agents (production planning, quality analytics, predictive maintenance, scheduling) direct and schedule the robot's work.
- Verified safety layer — validates, refuses, or overrides unsafe tool calls independently of the brain (people and equipment protected).
Accountability boundary
Safety lockout, final quality release, labor relations, hazardous-process authorization, and plant leadership remain human-accountable.
These remain human-owned. The robot executes within an engineered envelope and routes anything outside it — quality-standard deviations, tooling changes with scrap risk, or unsafe conditions — to the accountable human.
Operating and safety procedure
- Confirm the cell/line is mapped, people and equipment are protected, and the task is within the engineered envelope.
- The brain plans and emits motor-primitive tool calls; the safety layer validates each before execution.
- Execute within speed, force, reach, and collaborative-safety limits via VLA policies.
- Report progress, outcomes, exceptions, and any safety event to the sector agents and human owner.
- Stop and yield to humans for out-of-distribution parts or conditions, quality drift, or anything outside the envelope.
Architecture-specific failure modes
- Hallucinated or unsafe tool calls — the LLM brain issues a wrong or dangerous action. Mitigation: a verified low-level safety layer that validates every tool call against the physical envelope and can refuse it.
- Sim-to-real gap — world-model / robot-gym training diverges from reality. Mitigation: conservative behavior on out-of-distribution inputs, real-world evaluation, graceful degradation.
- Reward hacking from RLAIF — the AI critic is gamed, yielding behavior that scores well but is unsafe. Mitigation: diverse critics, human spot-checks, outcome-based evaluation.
- Physical-world prompt injection — adversarial signs, audio, or objects manipulate the brain. Mitigation: treat perceived instructions as untrusted; require authenticated commands for high-consequence actions.
- Fleet model-monoculture — a shared brain fails in lockstep across many robots. Mitigation: model diversity, staged rollouts, manual fallback.
Labor-market grounding (how these roles are advertised)
The human roles this operating system staffs appear on job boards with concrete, checkable signals. The AI-personnel and robot skills here are designed to support these advertised roles, not to replace the accountable human in them.
- Advertised titles & seniority ladder: Operator/assembler → technician/setup → process/quality engineer → production supervisor → plant manager; maintenance apprentice → journeyman → reliability engineer.
- Skills, tools & tech employers list: MES, ERP (SAP), PLC/SCADA, CAD/CAM, SPC/quality (Minitab), CMMS, industrial robotics, Lean/Six Sigma.
- Qualifications, certifications & licenses: Six Sigma Green/Black Belt, ASQ CQE/CQA, PE, CMfgE, PMP, OSHA/forklift, journeyman trades.
- KPIs / metrics in postings: OEE, scrap/defect rate (PPM), on-time delivery, downtime/MTBF, safety TRIR.
- Where these roles are posted: Indeed, LinkedIn, ZipRecruiter, manufacturing boards, Snagajob (hourly).
Grounding reflects 2026 job-posting conventions across LinkedIn, Indeed, Dice, ZipRecruiter, Glassdoor, USAJOBS, GovernmentJobs, and specialized boards, spot-verified against public listings and O*NET/BLS. Re-verify specifics — especially pay, certifications, and licenses — against live postings before operational use.
Adapting to any nation (context modifiers)
In industrializing economies this role can raise the productivity of small job shops without full-line automation capital — shared or leased cells matter most. In high-income plants it fills chronic machining and assembly trade shortages and enables lights-out shifts with human oversight by day. Re-read through:
- Scale (city-state → federation): whether this role is unified or layered across local/regional/national tiers.
- State capacity (fragile → high-capacity): whether the owning institution exists and can be held to account, or the job is met by markets, households, NGOs, or donors.
- Income level (low → high): affordability of automation and the balance of subsistence vs. wage work.
- Formality (informal → formal): whether the people and assets this role acts on appear in any registry at all.
- Resource & geography: which hazards and dependencies dominate (water-scarce, flood-prone, landlocked, trade-dependent).
- Political system & legitimacy: where the human-accountability boundary actually binds and who may hold power to account.
