Learning Options for Compositional Motor Control with Adapter Banks

2026-09-15

AI Summary

研究通过使用共享循环核心和残差适配器库学习灵活的运动基元，解决了如何学习神经科学理论中提出的系统问题。

Abstract

Learning flexible motor primitives is a hallmark of skilled motor control. Recent neuroscience theory proposes that motor primitives may be implemented as low-rank perturbations of a shared recurrent network, but leaves open how such a system is learned. We translate this principle into a novel architecture for learning motor skills end-to-end: a shared recurrent core modulated by a bank of residual adapters, each selected by a discrete latent code. Trained on closed-loop biomechanical control, the adapters develop emergent low-rank perturbations of the recurrent dynamics despite no architectural rank constraint, placing task representations in disparate subspaces of the shared core network. A simple high-level policy over the learned options, optimized while the whole network is frozen, sequences the low-rank adapters to produce novel out-of-distribution movements. We demonstrate the ability to generalize to novel motor sequences within the closed-loop control setting, improving on the generalization error of a task-input-conditioned multitask baseline by upto order of magnitude.

Problem

• flexible motor primitives
• low-rank perturbations
• shared recurrent network

Innovation

• low-rank perturbations
• residual adapters
• shared recurrent core
• discrete latent code
• closed-loop control