Learning Options for Compositional Motor Control with Adapter Banks

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.

Machine Learning (cs.LG); Robotics (cs.RO); Neurons and Cognition (q-bio.NC)

arXiv:2609.17042 [cs.LG]

(or arXiv:2609.17042v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.17042 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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