  Recent work has demonstrated that motion planners' performance can be
significantly improved by retrieving past experiences from a database.
Typically, the experience database is queried for past similar problems using a
similarity function defined over the motion planning problems. However, to
date, most works rely on simple hand-crafted similarity functions and fail to
generalize outside their corresponding training dataset. To address this
limitation, we propose (FIRE), a framework that extracts local representations
of planning problems and learns a similarity function over them. To generate
the training data we introduce a novel self-supervised method that identifies
similar and dissimilar pairs of local primitives from past solution paths. With
these pairs, a Siamese network is trained with the contrastive loss and the
similarity function is realized in the network's latent space. We evaluate FIRE
on an 8-DOF manipulator in five categories of motion planning problems with
sensed environments. Our experiments show that FIRE retrieves relevant
experiences which can informatively guide sampling-based planners even in
problems outside its training distribution, outperforming other baselines.
