| name | capopt-sim-to-real-domain-randomization-engineer |
| description | Capability/optimization role: **Sim-to-real & domain-randomization engineer** (Human engineering role (AI/robotics)) — closes the gap between simulation/world-model training and hardware. Part of the layer that decides *how* robot and machine capabilities are built — across model tiers (LLM, SLM, tiny LM, deterministic) and many training methods (imitation, model-based/offline RL, RLHF/RLAIF, sim-to-real, distillation, classical control, formal methods). Use this skill when choosing or building how a capability is trained, optimized, or run on-device, even if the user only describes the underlying need. Works under a simulation lead. |
Capability & Optimization — Sim-to-real & domain-randomization engineer
Layer: Capability / optimization spectrum (how robots and machines are made capable) · Type: Human engineering role (AI/robotics)
Human supervisor: simulation lead · Used by: ../embodied-ai-stack/, ../autonomous-fleet-ops/, robot & machine skills · Shared concepts: ../../00-framework/SKILL.md
What this role is
The Sim-to-real & domain-randomization engineer closes the gap between simulation/world-model training and hardware. Domain randomization, system identification, and real-world fine-tuning; an exhaustive simulation regime can pre-train most behavior cheaply before any field data.
Why this layer exists
RLAIF is one way to make an embodied system capable — not the only or always the best one. Capability is right-sized per task across a heterogeneous stack and a spectrum of methods. This role owns the part of that spectrum described above, and works with the build-side roles in _catalogs/embodied-ai-stack/ and the operations roles in _catalogs/autonomous-fleet-ops/.
The capability/optimization spectrum (shared model)
Capability is right-sized per task, not delivered by one big model trained one way. CivStack assumes a heterogeneous capability stack and a spectrum of optimization methods:
Model tiers (right-sized compute).
- LLM / large multimodal models — deliberation, language tasking, long-tail reasoning, and planning (cloud or high-end edge).
- SLMs (small language / vision-language models) — on-device reasoning and perception at lower cost and latency.
- Tiny LMs / specialized nets — fast reactive perception and control within tight power and latency budgets.
- Deterministic controllers — PID, MPC, state machines, planners, and convex/MILP optimization for hard-real-time, verifiable, safety-critical loops.
A capability is assigned to the smallest, most deterministic tier that meets its accuracy, latency, and safety needs; the large model is invoked only when needed (cascade / routing).
Optimization methods (exhaustive ↔ efficient).
- Imitation / behavior cloning (BC, DAgger, inverse RL) — data-efficient bootstrap from demonstrations.
- Model-based RL & world models — learn a simulator and plan/imagine in it; sample-efficient.
- Offline RL — learn from logged data without risky online exploration.
- RLHF / RLAIF / rule-based & constitutional rewards — preference and reward shaping; RLAIF is one option, not the only one.
- Sim-to-real — massively parallel simulation, domain randomization, and system identification.
- Self-supervised & representation learning — pretrain from unlabeled data.
- Supervised fine-tuning & distillation — specialize and shrink (LLM → SLM → tiny LM).
- Quantization / pruning / sparsity — compress for the edge.
- Search & planning (MCTS, MPC, graph/sampling planners) — deterministic, verifiable run-time decisions.
- Classical optimization & control (convex, MILP, optimal control) and formal methods / verification — guarantees that statistical learning cannot give.
- Evolutionary / black-box search — when gradients are unavailable.
Selection rubric. Choose by exhaustiveness vs efficiency (compute and data budget), determinism and verifiability (safety-criticality), latency and power (on-device vs offloaded), data availability (demos vs logs vs sim), and reversibility/consequence. Safety-critical and hard-real-time loops favor deterministic, verifiable methods; open-ended judgment favors large learned models; most real systems are hybrids with a verified deterministic safety layer beneath learned policies. The roles that design and run this spectrum are in _catalogs/capability-optimization/.
When to use this skill
Use it when a task calls for this work: closes the gap between simulation/world-model training and hardware. Pair with the robot skills (_catalogs/humanoid-robots/, <sector>/robots/) and machine skills (_catalogs/autonomous-machines/, <sector>/autonomous/) whose capabilities are being trained, optimized, or deployed.
Decision rights & accountability
- Owns the technical quality, efficiency, and robustness of this method/layer.
- Justifies the method and model-tier choice against the selection rubric (exhaustiveness vs efficiency, determinism, latency, verifiability).
- Gates promotion to production with the safety and evaluation leads.
How this role chooses (selection discipline)
- State the capability, its accuracy bar, latency/power budget, and safety-criticality.
- Pick the smallest, most deterministic model tier that can meet it (deterministic → tiny LM → SLM → LLM).
- Pick the most efficient optimization method that reaches the bar with available data (demos → sim → logs → online).
- Reserve large learned models for open-ended judgment; reserve deterministic/verified methods for safety-critical loops.
- Measure on the real task, compare tiers/methods, and keep a verified safety layer beneath anything learned.
Decision tool: use the routing matrix in docs/capability-routing-matrix.md and the interactive selector tools/capability-router.html to turn a capability's constraints (safety, latency, verifiability, task type, data, compute, connectivity) into a recommended tier, method, and fallback.
Failure modes and safeguards
- Over-reach — using a large learned model where a verifiable controller would be safer and cheaper. Mitigation: the selection rubric and a verified safety layer.
- Reward hacking / spec gaming — learned objectives gamed. Mitigation: diverse signals, human spot-checks, outcome-based evaluation.
- Sim-to-real and distribution shift — training diverges from deployment. Mitigation: shadow mode, staged rollout, monitoring.
- Efficiency/quality regressions — compression or routing degrades behavior silently. Mitigation: continuous benchmarking across tiers.
Adapting to any nation (context modifiers)
Compute, data, and connectivity budgets vary enormously; lower-resource settings push capability toward smaller, on-device, and deterministic methods, and toward distillation of expensive models into cheap ones. 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.
