General Robot Learning
General robot learning is the goal of one system that handles many tasks, objects and scenes without being rebuilt for each. The dominant route pursues it by scaling data and parameters. Teach-and-Grow Learning takes a complementary route: hold the pretrained stack fixed and let explicit, reusable skills accumulate instead.
Why generality is the hard part
A robot that has learned one pick-and-place task has learned very little about the next object. Physical interaction data is expensive in a way text and code are not: it has to be created by operating a machine. Object pose, camera geometry, clutter, material and embodiment all interact, and covering one factor does not cover their combinations. A general system has to generalise over that product, not over a single axis.
Route one: scale
Train a large policy on a broad cross-embodiment corpus so that its representations transfer, then fine-tune per task. This is the route behind modern vision-language-action models, and it works. Its cost is structural: a new capability is absorbed into shared parameters, so repairing one behaviour means re-checking the others, and the expense recurs with every change of object, sensor or gripper.
Route two: explicit capability
The complementary route does not try to make one set of weights cover everything. It keeps the pretrained prior and grows an explicit store of validated behaviours, each with a stated scope and an outcome test, plus a memory of the conditions under which each was used. Generality then comes from retrieval and composition rather than from parameter coverage.
The two routes are not exclusive. A learned policy can implement one of the explicit skills; a geometric planner can bridge two of them. What changes is where new task knowledge is written.
What route two costs
It moves the difficulty rather than removing it. Grounding, validation, compatibility checking and retrieval all have to stay manageable, or the explicit store becomes as expensive to grow as the parameters were to retrain. TGL analyses those cost regimes rather than treating cheap growth as automatic.