Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions
Constantinos Chamzas Zachary Kingston Carlos Quintero-Peña Anshumali Shrivastava Lydia E. Kavraki Constantinos Chamzas Zachary Kingston Carlos Quintero-Peña Anshumali Shrivastava Lydia E. Kavraki
Earlier work has shown that reusing experience from prior motion planning problems can improve the efficiency of similar, future motion planning queries. However, for robots with many degrees-of-freedom, these methods exhibit poor generalization across different environments and often require large datasets that are impractical to gather. We present SPARK and FLAME, two experience-based frameworks...