  Autonomous service robots require computational frameworks that allow them to
generalize knowledge to new situations in a manner that models uncertainty
while scaling to real-world problem sizes. The Robot Common Sense Embedding
(RoboCSE) showcases a class of computational frameworks, multi-relational
embeddings, that have not been leveraged in robotics to model semantic
knowledge. We validate RoboCSE on a realistic home environment simulator
(AI2Thor) to measure how well it generalizes learned knowledge about object
affordances, locations, and materials. Our experiments show that RoboCSE can
perform prediction better than a baseline that uses pre-trained embeddings,
such as Word2Vec, achieving statistically significant improvements while using
orders of magnitude less memory than our Bayesian Logic Network baseline. In
addition, we show that predictions made by RoboCSE are robust to significant
reductions in data available for training as well as domain transfer to
MatterPort3D, achieving statistically significant improvements over a baseline
that memorizes training data.
