Lifelong Robot Learning
Lifelong robot learning is the goal of a robot that keeps acquiring new tasks throughout its working life rather than being trained once and frozen. The central difficulty is that learning a new task must not erase an old one. Teach-and-Grow Learning addresses that by keeping new capability in explicit stores instead of shared parameters.
The forgetting problem
When a policy absorbs a new behaviour by updating its parameters, the update touches weights that also support everything learned before. The result can be degraded performance on earlier tasks — the classic stability-plasticity trade-off — and the usual mitigation is rehearsal or regularisation, both of which cost something.
In robotics the practical symptom is worse than a metric drop: a robot that was reliable on one task becomes unreliable on it after being taught another, and the failure may only surface in the field.
An alternative mechanism
If new knowledge is stored as an explicit object rather than written into weights, then adding it does not overwrite anything. TGL's Skill Library holds validated behaviours with their scopes, and Experience Memory holds the conditions and repairs. Acquiring a new task adds to those stores.
This does not make forgetting impossible. Retrieval can still pick the wrong block, and grounding can fail in a new scene. What it removes is the mechanism by which learning one thing directly damages another.
What has to be true for this to work
Two conditions matter. Entries need stated scopes, so that an overgeneralised skill can be narrowed rather than silently misapplied. And compatibility has to stay checkable: if every new skill must be validated against every existing one, growth becomes quadratic and the advantage disappears. The paper analyses these regimes explicitly.
The scaling hypothesis
The report proposes a hypothesis relating effective reusable experience to future-task error and teaching demand, both falling toward irreducible floors. It is presented as a hypothesis to be tested over sequential acquisition experiments, not as a fitted law.