Learning Options for Compositional Motor Control with Adapter Banks
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 netwo...
| Authors | Sreejan Kumar, Marcelo Mattar, Lea Duncker |
|---|---|
| Primary category | cs.LG |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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