The IPJR paradigm will simplify assembly tasks for various missions, enabling the construction of large solar arrays, large space observatories, and large human habitats (both in space and on a planetary surface). The variety of objects that IPJRs may be able to grasp may also enable the use and assembly of ISRU components in construction. Additionally, servicing, repair, and disassembly tasks may be easier to perform with the IPJR paradigm, including performing said tasks on uncooperative spacecraft. The machine learning tasks required to perform assembly (separable into supervised, unsupervised, and reinforcement learning) have broad applicability in all robotic and autonomous tasks. In addition to assembly, the developed algorithms will have direct applicability to servicing and repair tasks, and may potentially contribute to increasing the permissible autonomy for all unmanned space missions.
Space robotic assembly will be crucial for large scale construction, including high capacity solar arrays requiring assembly and servicing, large space telescopes with apertures greater than 25 m in diameter, and servicing facilities.
The commercial space industry desires servicing capabilities to repair and refurbish spacecraft already in orbit. Advances in space robotic assembly research translate to advances in servicing.