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Title: Learning to Retrieve Relevant Experiences for Motion Planning
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 representation 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.  more » « less
Award ID(s):
1718478
NSF-PAR ID:
10379998
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
2022 International Conference on Robotics and Automation
Page Range / eLocation ID:
7233 to 7240
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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