Motion planning is not the kind of well behaved task where you can change an actuator type and everything just works. And modern robotics specifically, the kind where demands on manipulation capability are the highest, is dealing with open ended environments - where the environment, the task and the objects involves are generally unknown in advance.
Having "strong generalists" like Astra helps bootstrap a lot of things, but that still isn't a "full solve". You don't get to go from "a series of hardcoded commands that use this actuator open a bottle in a sim" to "a set of behavioral heuristics that tell a robot AI how to use this actuator effectively for performing arbitrary operations on unseen objects" for free.
Which is why the dynamics of generalization and transfer learning between actuators are so important. If you need very little data to "bootstrap" a new actuator type, and even a few sim envs can get a robot to perform the tasks it knows from other effector embodiments with it, and start taking advantage of what the new kinematics enable? You're in a very good shape. If transfer barely works, and you need 100000 hours of real world embodied data on diverse tasks per actuator? Living hell.