Neuro-Inspired Inverse Learning for Planning and Control

Our Inverter framework conceptualizes the brain as an inversion machine — using the same principles that allow the brain to control behavior so effectively, Inverters let neural networks plan whole action sequences in a single pass — matching or surpassing the scores of standard methods in tasks from maze navigation to quantum-gate synthesis, at one to three orders of magnitude less inference compute time. Inverters offer a versatile world-interface for a wide range of AI applications, especially for latency- and resource-critical embodied AI.

The brain as an inversion machine

The dominant view in neuroscience frames the brain as an inference machine — figuring out what's out there from incomplete sensory data. We propose a complementary view: the brain is, first and foremost, an inversion machine. Goal-directed behavior poses the inverse problem — given a desired outcome, which actions realize it? — and acting is, at its computational core, inverting a model of how the world responds.

Our Inverter framework takes that view literally. An Inverter is a neural network that, given a state and a goal, outputs the full action sequence in a single pass — trained by backpropagating the task objective through a paired learned forward model of the world's dynamics. The same paired forward/inverse architecture has long been argued to underlie biological motor control.

Three brain-inspired principles

The framework is organized around three principles drawn from how the mammalian brain achieves fast, effective goal-directed behavior:

1. Paired forward and inverse models. A learned model of how the world responds to action gives an exact training signal to a second network that maps goals back to actions. The pairing is the inversion.

2. Open-loop multi-step motor commands. The Inverter emits a whole action chunk ballistically in one pass — too fast for sensory correction, the way your motor cortex commits to a reach upfront. Hundreds of sequential decisions per episode collapse into a handful.

3. Sequential, hierarchical organization of action. Behaviors are segmented into sequential sub-plans and nested across levels of timescale and abstraction: a higher-level Inverter emits subgoals that a lower-level Inverter realizes. The same recursive shape supports radically different domains — mirroring how the same mammalian motor framework supports independent finger control in primates, millisecond-scale echolocation in bats, rhythmic whisking in rodents, and a hydrostatic trunk in elephants.

The result — from maze navigation to quantum control

On the three maze2d and six antmaze D4RL benchmarks for navigation and locomotion, single Inverters or hierarchical n=2 Inverter stacks match or improve on every comparable offline-RL and diffusion-planner baseline — on average +24.2 % better (range −1.9 % to +78.2 %), at one to two orders of magnitude less inference compute time. The same single-pass paradigm extends cleanly outside robotics: a Pulse Inverter synthesizes the microwave pulses that implement an arbitrary single-qubit quantum operation on a realistic noisy chip. The standard tool is GRAPE, an iterative numerical optimizer. The Inverter matches GRAPE on fidelity — and produces each pulse in 2.1 ms instead of 5.6 s, a ~2700× reduction in per-gate compute time.

What the Inverter framework enables

The Inverter framework is applicable to a wide range of important application areas:

• Robotics & embodied AI — manufacturing, mobile robots, surgical systems
• Medical applications — optimization of cancer therapy regimens
• Drone & autonomous-vehicle control — low-latency planning at the edge
• Quantum computing — variational circuits, quantum error correction, adaptive feedback
• Industrial process control — tight-latency control loops on embedded hardware

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