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cs.RO2025
Learning Attentive Neural Processes for Planning with Pushing Actions
Atharv Jain, Seiji Shaw, Nicholas Roy
Our goal is to enable robots to plan sequences of tabletop actions to push a block with unknown physical properties to a desired goal pose. We approach this problem by learning the…
cs.RO2024
Towards Practical Finite Sample Bounds for Motion Planning in TAMP
Seiji Shaw, Aidan Curtis, Leslie Pack Kaelbling +2
When using sampling-based motion planners, such as PRMs, in configuration spaces, it is difficult to determine how many samples are required for the PRM to find a solution consiste…