What problem does it solve? Robotics code fails in ways ordinary software does not: physical damage, missed real-time deadlines, sensor noise, and sim-to-real gaps. This Skill provides twelve concrete design principles with code examples so robot software modules stay decoupled, safe, testable, and reusable across hardware platforms. ## Core Features & Use Cases - SOLID for Robotics: Adapts Single Responsibility, Dependency Inversion, Open-Closed, Interface Segregation, and Liskov Substitution to hardware interfaces like arms, grippers, and cameras, with Python ABC examples. - Robotics-Specific Principles: Covers separation of rates (control loops vs. perception threads), fail-safe defaults, idempotent commands, configuration over code, structured telemetry, composable skills, and graceful degradation. - Code Review Checklist: A 12-item quick-reference table for reviewing robotics modules, e.g. checking whether perception blocks the control loop or whether commands are safe to retry. - Use Case: When refactoring a perception-planning-control pipeline or designing a hardware abstraction layer so the same task code runs on a real UR5 and a MuJoCo simulation, apply these principles to structure interfaces and dependency injection. ## Quick Start Ask the AI to review your robot control module against the robotics software design principles and suggest refactoring toward hardware-independent interfaces.
