[
    {
        "id": "osp-26724",
        "type": "article-journal",
        "title": "Learning Options for Compositional Motor Control with Adapter Banks",
        "author": [
            {
                "family": "Kumar",
                "given": "Sreejan"
            },
            {
                "family": "Mattar",
                "given": "Marcelo"
            },
            {
                "family": "Duncker",
                "given": "Lea"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/learning-options-for-compositional-motor-control-with-adapter-banks",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]