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

Evaluating Continual Learning on a Home Robot