| name | robot-21-eldercare-support-robot |
| description | Humanoid/embodied robot role for the Household operating system: **Eldercare support robot** — steady mobility, fetch items, monitor for falls, and prompt daily routines while intimate care stays human. Best in: aging-in-place homes, assisted living, community eldercare. 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 household sector; trigger whenever the task needs this hands-on work, even if the user only describes the underlying need. |
Eldercare support robot
Operating system: 21. Household, Childcare, Eldercare, and Community Support · Personnel type: LLM-brained embodied robot
Best environments: aging-in-place homes, assisted living, community eldercare
Sector skill: ../../SKILL.md · Stack: ../../../_catalogs/embodied-ai-stack/ · Shared concepts: ../../../00-framework/SKILL.md
What this role is
The Eldercare support robot is an embodied robot whose job is to steady mobility, fetch items, monitor for falls, and prompt daily routines while intimate care stays human. Extends independent living: a steady arm for walking and standing, fall detection and gentle-assist response, fetching glasses and medications (prompting, never administering by force), and routine reminders shaped by the person's own preferences. Companionship and intimate care remain human by design — this robot buys time for them, it does not substitute for them.
Operating-system context
This role serves the Household operating system, whose mission is to reproduce daily life: raise children, care for dependents, maintain homes, and prevent isolation. It takes physical chores and fetch-and-carry support so household members and caregivers 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 "steady mobility, fetch items, monitor for falls, and prompt daily routines while intimate care stays human" in environments such as aging-in-place homes, assisted living, community eldercare. 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 (household / family caregiver / care coordinator) — owns care decisions, privacy and consent, household norms, and exceptions; holds override and stop authority.
- LLM brain — perceives the home, plans the task, and issues motor-primitive tool calls (
navigate_to, grasp, pick, place, inspect).
- VLA policies — execute dexterous, delicate manipulation (e.g., folding laundry or carrying a meal tray without spills) under the engineered safety envelope.
- AI agents — the sector's planning/monitoring agents (care scheduling, medication reminders, household logistics, wellbeing check-ins) direct and schedule the robot's work.
- Verified safety layer — validates, refuses, or overrides unsafe tool calls independently of the brain (residents, children, and pets protected).
Accountability boundary
Parenting, intimate-care consent, safeguarding, abuse detection, emotional bonding, and end-of-life care require human responsibility.
These remain human-owned. The robot executes within an engineered envelope and routes anything outside it — care concerns, consent or privacy questions, or unsafe conditions — to the accountable human.
Operating and safety procedure
- Confirm the home is mapped, residents, children, and pets 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 in-home 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 situations, distress or refusal by the person assisted, 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: Caregiver/aide → senior aide/lead → care coordinator → program manager; social work: BSW → MSW/LCSW → supervisor.
- Skills, tools & tech employers list: Scheduling/EVV systems, care-plan and family-communication apps, case-management systems, benefits portals.
- Qualifications, certifications & licenses: CNA, HHA, CPR/First Aid, CDA (child development), LSW/LCSW, Community Health Worker certification, background checks.
- KPIs / metrics in postings: Client safety/falls, satisfaction, care-plan adherence, placement/stability, caseload outcomes, response time.
- Where these roles are posted: Care.com, Snagajob, Indeed, GovernmentJobs (county social services), Idealist (nonprofit), local agencies.
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)
Affordability decides everything here: shared building-level or community-owned units, subsidized care deployments, and rental models precede private ownership in most of the world. Intimate and emotional care remains human by design, not merely by boundary. 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.
