Neuro-Inspired Inverse Learning for Planning and Control

2026-05-22

AI Summary

This work addresses the challenge of achieving efficient, low-latency planning and control in embodied intelligence by proposing the Inverter framework, which integrates neuroscience-inspired forward and inverse internal models, hierarchical action organization, and open-loop multi-step actions. The framework introduces a novel inverse learning paradigm that jointly trains learnable and analytical components through end-to-end differentiable optimization guided by a global objective function, enabling near-analytically optimal trajectory generation. Evaluated on nine maze environments from D4RL, Inverter improves performance by 24.2% on average while reducing inference computational cost by one to two orders of magnitude. In quantum gate synthesis tasks, it achieves over 1,000× acceleration relative to the GRAPE baseline while matching its accuracy.

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 natural by analytic or algorithmic modules; we formalize IL and delineate it from supervised, reinforcement, and imitation learning. IL bridges Reinforcement Learning (RL)-style amortization, which runs in a single forward pass but emits only one action at a time, and Optimal Control (OC)-style sequence planning over whole trajectories, but with iterative test-time computation. Single Inverters or hierarchical n=2 Inverter stacks match or improve on offline-RL and diffusion-planner baselines on all 3 maze2d and 6 antmaze D4RL variants by an average of +24.2% (range -1.9% to +78.2%), at one-to-two orders of magnitude less inference compute time. Distinctively, optimizing through the Figure of Merit (FoM) over the entire T-step action sequence - rather than per step - lets Inverters produce smooth, goal-coherent, trajectory-wide structure and reach control policies closer to the analytic optimum than the policy underlying the training data itself. We also identify a failure mode of IL: FoM hacking under narrow training-data coverage, which we mitigate by using random training data with broader coverage. As an application example, a Pulse Inverter synthesizes arbitrary single-qubit quantum gates with fidelity matching the standard iterative numerical baseline (GRAPE), at more than 1000x lower per-gate compute time. In summary, we conclude that IL enables a versatile class of world-interfaces, especially for latency- and resource-critical embodied AI.

Problem

• embodied planning
• inverse learning
• trajectory optimization
• low-latency control
• action sequence coherence

Innovation

• Inverse Learning
• Neuro-Inspired Control
• Embodied Planning
• Action Sequence Optimization
• Figure of Merit