  Mobile manipulators always need to determine feasible base positions prior to
carrying out navigation-manipulation tasks. Real-world environments are often
cluttered with various furniture, obstacles, and dozens of other objects.
Efficiently computing base positions poses a challenge. In this work, we
introduce a framework named MoMa-Pos to address this issue. MoMa-Pos first
learns to predict a small set of objects that, taken together, would be
sufficient for finding base positions using a graph embedding architecture.
MoMa-Pos then calculates standing positions by considering furniture
structures, robot models, and obstacles comprehensively. We have extensively
evaluated the proposed MoMa-Pos across different settings (e.g., environment
and algorithm parameters) and with various mobile manipulators. Our empirical
results show that MoMa-Pos demonstrates remarkable effectiveness and efficiency
in its performance, surpassing the methods in the literature. %, but also is
adaptable to cluttered environments and different robot models. Supplementary
material can be found at \url{https://yding25.com/MoMa-Pos}.
