  Robots in home environments need to be able to learn new skills continuously
as data becomes available, becoming ever more capable over time while using as
little real-world data as possible. However, traditional robot learning
approaches typically assume large amounts of iid data, which is inconsistent
with this goal. In contrast, continual learning methods like CLEAR and SANE
allow autonomous agents to learn off of a stream of non-iid samples; they,
however, have not previously been demonstrated on real robotics platforms. In
this work, we show how continual learning methods can be adapted for use on a
real, low-cost home robot, and in particular look at the case where we have
extremely small numbers of examples, in a task-id-free setting. Specifically,
we propose SANER, a method for continuously learning a library of skills, and
ABIP (Attention-Based Interaction Policies) as the backbone to support it. We
learn four sequential kitchen tasks on a low-cost home robot, using only a
handful of demonstrations per task.
