Robot Arm Simulation: A Test Plan Before Hardware
Build a useful simulation test suite around interfaces, geometry, timing faults, sensors, and reproducible scenarios.
THE ARM LAB / TAG: MOTION PLANNING
KNOW WHICH WORLD YOUR PLAN DESCRIBES.
Coordinate frames, scene assumptions, perception context, and testable motion proposals.
A motion proposal depends on a model of the arm and its surroundings. Its usefulness changes when a tool, reference frame, observation, or scene object changes. This collection focuses on making that context visible before interpreting a plan as a reliable instruction.
Read the coordinate frames guide first when numbers pass between camera, base, tool, and rendering spaces. Continue to the AI planning guide when observations become proposed tasks. Use the simulation checklist to define reproducible fixtures and identify which assumptions a test actually exercises.
The articles emphasize interface reasoning, not a universal planner configuration. A drawing that reaches a point does not establish collision-free or physically feasible motion. The robot API overview explains the layers around a controller, while the AI overview connects perception to validation. Our local state library exposes named illustration poses only; it does not implement a motion planner, inverse kinematics, or a scene model.
Robot Arm Coordinate Frames and Kinematics, Explained
Make positions meaningful with explicit units, coordinate frames, tool offsets, and a clear distinction between poses and paths.
AI Robotic Arm APIs: From Perception to Verified Motion
Connect perception, scene state, planning, and execution without treating an AI prediction as permission to move.
A little code. A lot to explore.
MAKE YOUR NEXT MOVE.
Start in the browser. Understand the interface. Build with clearer boundaries.