Neuro-Inspired Inverse Learning for Planning and Control

“The mammalian brain achieves fast, highly effective goal-directed behavior leveraging paired forward/inverse internal models, open-loop multi-step motor commands, and the hierarchical organization of action.

Our findings show that the Inverter framework, built on the same three principles, enables fast and effective planning and control through a feedforward, sequence-level FoM-and-IM core that emits entire action sequences in single forward passes. We find that Inverters offer consistently high task performance at a fraction of the inference compute time used by step-wise RL or iterative planners.”