Neuro-Inspired Inverse Learning Framework for Embodied Planning
A new AI framework called Inverter, inspired by mammalian brain principles, has been introduced for embodied planning and control. It uses Inverse Learning (IL), a method distinct from supervised, reinforcement, and imitation learning, to bridge single-step amortization and full-trajectory optimal control. The framework employs paired forward/inverse models, open-loop multi-step commands, and hierarchical action organization. In tests on maze2d tasks, single or two-level Inverter stacks matched or outperformed offline-RL and diffusion-planner baselines. The paper is available on arXiv.
Key facts
- Framework named Inverter
- Based on three principles from mammalian brain: paired forward/inverse models, open-loop multi-step commands, hierarchical action organization
- Uses Inverse Learning (IL) trained end-to-end
- IL bridges Reinforcement Learning-style amortization and Optimal Control-style sequence planning
- Single Inverters or hierarchical n=2 Inverter stacks tested
- Matched or improved on offline-RL and diffusion-planner baselines on all 3 maze2d tasks
- Paper available on arXiv with ID 2605.24152
Entities
Institutions
- arXiv