It's impressive that something this simple can do this much. But those funky types of actuators all live and die by transfer learning now.

If a robot AI can figure out how to operate them with very little sim and teleop data, and learn to take advantage of their strengths while maintaining good performance on tasks learned from UMI datasets, teleop data or human headcam videos? Allowing the same "robot mind" to work with different actuators?

Then I would expect those to have a decent niche - sitting between the classic two finger UMI gripper and a humanoid hand. Not a drop-in replacement for a human hand, but still more dexterity per hand without sacrificing all of the ruggedness and mechanical simplicity.

If transfer learning for different actuator types doesn't work so well? I expect the field to collapse to a binary of "UMI gripper or humanoid hand", with nearly no in-between.

In general, I'm carefully optimistic? But we are yet to demonstrate with confidence that this kind of transfer for actuators with radically different kinematics would work.

Should be doable w/ existing AIs to generate a staged sequence of operations by taking a demonstration & deriving another sequence of operations for achieving the same outcome w/ another set of actuators. I'm not a roboticist but robotic arms have specifications & those specifications are basically generalized algebraic datatypes so Astra or even Grok should be able to generate programs for manipulating most objects.