Safety & Standards

Safety Constraints in Robotics

Safety constraints in robotics are explicit limits on robot behavior — joint position and velocity bounds, force and torque caps, workspace boundaries, and human-proximity rules — that a system must satisfy at all times during operation. In learning-based control, they are often formalized as constrained Markov decision processes (CMDPs), where a policy maximizes reward subject to bounds on expected constraint violations. Enforcement mechanisms range from hard-coded software limits to control barrier functions and runtime shielding.

Why it matters for physical AI

Learned policies offer no inherent guarantees, so explicit constraint enforcement is what makes it acceptable to run neural network controllers on physical hardware around people and property.