MOSAIC: Multi-task Orchestration via Sparse Atomic Interpolated Control

Multi-task robot learning is not limited only by policy capacity, but also by the structure through which tasks share control. Dense sharing couples incompatible task dynamics in a common pathway, while expert isolation reduces transfer and scales poorly. We introduce MOSAIC, a sparse basis-sharing framework for scalable multi-task manipulation. MOSAIC learns a shared low-coherence dictionary of atomic control bases and infers, for each context, a sparse set of active bases with continuous coefficients through a deterministic expected spike-and-slab bottleneck. The resulting active basis span allows related task phases to reuse control factors while reducing update coupling among incompatible dynamics. To avoid mode averaging in continuous mixtures, MOSAIC aligns dictionary bases with temporal options and uses continuous skill coordinates to modulate low-level control. Across Meta-World MT-10 and MT-50, MOSAIC achieves strong multi-task performance, improves hard contact-rich tasks, and preserves single-pass closed-loop execution efficiency. Mechanism-level analyses, including ablations, sparse-routing diagnostics, update-coupling measurements, and basis interventions, support that the gains arise from structured selective sharing rather than increased capacity or task-specific parameter isolation. All code, dataset, and reproducibility scripts will be released.