What does TGL stand for?
Teach-and-Grow Learning. The paper is “Teach and Grow: An Agent-Centered Architecture for General Robot Learning”.
Teach-and-Grow Learning (TGL)
Teach-and-Grow Learning (TGL) is a training-free architecture for general robot learning. A pretrained multimodal AI Agent reads a few successful demonstrations, expresses their shared structure as closed-loop Skill Blocks, grounds each block in the current scene, and keeps the behaviours that pass validation. The pretrained model weights do not change.
The name in two halves
Teach is the input: a small number of demonstrations that reveal the subgoal sequence and the conditions worth checking. Teaching supplies structure, not the physical realization — the teacher's exact trajectory and pixel coordinates are deliberately not what gets retained.
Grow is the output: each task adds validated behaviour to an explicit Skill Library and the context of the attempt to Experience Memory. The next task starts from a larger base of inspectable capability, so the resource grows after deployment rather than only at training time.
What makes it training-free
Acquiring the incoming task invokes no gradient update, no fine-tuning, and no reinforcement-learning stage. The AI Agent and its specialist models may already be pretrained — that is assumed. What changes during task acquisition is the explicit skill and memory state, not the weights. This is the precise sense in which the term is used here, and it is narrower than “a model that was not trained at all”.
Why the separation matters
In an end-to-end policy, repairing one failure means changing parameters that also support everything else, followed by regression checks. There is no separately addressable fix for one object or one contact condition. TGL makes the repair a local edit to an explicit object: a Skill Block can be narrowed, its recovery rule changed, or an incompatible executor version flagged, without reopening the rest of the system.
What it does not claim
TGL does not claim that stored files guarantee retained behaviour: the right block must still be retrieved, grounded with current sensing, and executed successfully. It does not claim that low-cost growth is automatic, because unrestricted pairwise compatibility checks between skills can make a library expensive to grow. And the scaling hypothesis relating reusable experience to falling future-task error is presented as a hypothesis, not as a fitted law.
Frequently asked questions
How is TGL different from fine-tuning a robot policy?
Fine-tuning changes model parameters to absorb a new behaviour, which can affect previously supported behaviour and requires regression checking. TGL leaves parameters fixed and stores the new capability as an explicit, inspectable Skill Block.
What does a TGL run actually produce?
Two persistent stores: a Skill Library of validated executable behaviours with their scopes and contracts, and an Experience Memory recording the task, the selected blocks, observations, the outcome, the diagnosis and any repair.
How does TGL retain capabilities across tasks?
By writing them into explicit stores instead of into weights. A behaviour that validates on cases kept separate from the teaching demonstrations is admitted to the Skill Library with its scope and outcome test; the conditions, outcome, diagnosis and repair of each attempt go to Experience Memory. Later tasks retrieve from both, so the second attempt at a task starts from what the first one established.