AI en robotica

A robot’s learned policy must operate within hardware, environment, and safety constraints. A successful simulation or demonstration does not prove safe behavior around unfamiliar objects or people.

Key takeaways

• Define physical constraints and stop conditions.
• Test simulation-to-reality transfer.
• Protect the action path and verify outcomes.

Define the task, workspace, action limits, and safe stop conditions. Perception errors can cause a correct plan to act on the wrong object; control errors can make a correct target unsafe. Keep the model’s proposal separate from the controller and hardware interlocks that limit motion. Evaluate across objects, lighting, surfaces, camera positions, and starting states. Simulation can accelerate testing but may omit friction, sensor noise, damage, or human behavior. Measure task success, collisions, near misses, recovery time, and operator workload, not only a completion percentage. A robot foundation model may transfer skills across hardware or tasks, but transfer needs evidence for the intended embodiment. Record the robot, firmware, policy version, calibration, and environment. Provide a manual stop and a supervised mode for uncertain or high-consequence actions. Secure the control path. Restrict who can issue commands, validate tool inputs, and verify the physical state after an action. A text description of an action is not authorization to perform it.

1. Imagine a model proposes moving a box to a target location, but the camera misses a person entering the workspace.
2. A safety controller should stop or limit the motion even though the plan is syntactically valid.
3. Test the boundary case and verify the physical stop before evaluating task efficiency.

The constructed example shows why learned planning cannot replace hardware and operational safety controls.