@taskmaster4450le's thread

The researchers note that imitation learning — in which the agent learns by following an individual performing a task — can fail when small challenges are introduced. These could be things like lighting, a different setting, or new obstacles. In those scenarios, the robots simply don’t have enough data to draw upon in order to adapt.

The team looked to models like GPT-4 for a kind of brute force data approach to problem solving.