Robot Learning

Constraint Learning

Constraint learning is the inference of task or safety constraints, such as regions to avoid, forces not to exceed, or invariants to maintain, from demonstrations, corrections, or environment interaction, rather than specifying them manually. Approaches include inverse constraint learning from demonstrations assumed to be constraint-satisfying, learning constraint manifolds for planning, and extracting geometric task constraints like axis alignment from a handful of examples.

Why it matters for physical AI

Explicit learned constraints complement end-to-end policies by encoding hard requirements that must hold even off-distribution, supporting safety filters and verifiable behavior in deployed systems.