Skill Library
The Skill Library is the persistent store of validated behaviours in Teach-and-Grow Learning. It holds Skill Blocks that can be selected and run — their subgoals, supported conditions, grounding rules, compatible tools and verification logic — and it grows when a candidate passes validation, not every time the robot runs an episode.
What the library holds
A text description of “how to grasp” is not enough. Each entry has to connect an intent to an executor and to a test of its effect, together with the conditions under which the behaviour is claimed to work. That combination is what makes an entry selectable and runnable rather than merely descriptive, and it is what makes the scope of the claim inspectable.
How it grows
A candidate block is tested on cases kept separate from the teaching demonstrations. The test asks whether it produces the intended effect from the current scene, whether it preserves matched old behaviour, and whether it stays within its claimed scope. A weak block is narrowed or repaired; a validated one enters a new library version. Library growth is therefore a sequence of explicit decisions rather than an accumulation of episodes.
Why growth has a cost
Adding a skill locally can avoid reopening the whole learning system — but only if grounding, validation, compatibility and retrieval remain manageable. Unrestricted pairwise compatibility checks between skills can themselves make the library expensive to grow. The paper analyses these different cost regimes instead of treating cheap growth as automatic.
Inspectability
Because entries are explicit, a person can narrow an overgeneralised scope, change a recovery rule, or mark an executor version as incompatible with a block. Two things are deliberately kept apart: storing a behaviour, and that behaviour actually working later. The second still depends on retrieval, grounding with current sensing, and successful execution.