Neuro-Inspired Inverse Learning for Planning and Control
The article presents a neuro-inspired framework for planning and control in artificial intelligence. It introduces Inverse Learning (IL), which enhances goal-directed behavior by utilizing learned components and optimizing action sequences. The framework shows significant improvements in performance and efficiency compared to existing methods.
- ▪The Inverter framework is based on principles from the mammalian brain that support effective goal-directed behavior.
- ▪Inverters demonstrate an average improvement of +24.2% over offline-RL and diffusion-planner baselines while requiring significantly less computational time.
- ▪IL allows for the production of smooth, goal-coherent trajectories that approach analytic optimality.
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Computer Science > Artificial Intelligence arXiv:2605.24152 (cs) [Submitted on 22 May 2026] Title:Neuro-Inspired Inverse Learning for Planning and Control Authors:Maryna Kapitonova, Tonio Ball View a PDF of the paper titled Neuro-Inspired Inverse Learning for Planning and Control, by Maryna Kapitonova and Tonio Ball View PDF HTML (experimental) Abstract:We present a neuro-inspired framework for embodied planning and control. Building on three principles that enable fast and highly effective goal-directed behavior in the mammalian brain - paired forward/inverse internal models, open-loop multi-step motor commands, and sequential, hierarchical organization of action - our Inverter framework uses learned components, trained end-to-end through Inverse Learning (IL) and supplemented where…
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