  Sequences and time-series often arise in robot tasks, e.g., in activity
recognition and imitation learning. In recent years, deep neural networks
(DNNs) have emerged as an effective data-driven methodology for processing
sequences given sufficient training data and compute resources. However, when
data is limited, simpler models such as logic/rule-based methods work
surprisingly well, especially when relevant prior knowledge is applied in their
construction. However, unlike DNNs, these "structured" models can be difficult
to extend, and do not work well with raw unstructured data. In this work, we
seek to learn flexible DNNs, yet leverage prior temporal knowledge when
available. Our approach is to embed symbolic knowledge expressed as linear
temporal logic (LTL) and use these embeddings to guide the training of deep
models. Specifically, we construct semantic-based embeddings of automata
generated from LTL formula via a Graph Neural Network. Experiments show that
these learnt embeddings can lead to improvements in downstream robot tasks such
as sequential action recognition and imitation learning.
