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Recent progress in robot learning has produced impressive results, yet many systems still require learning from large datasets of demonstrations and are less effective in clutter or with highly deformable objects. This talk presents work on data-efficient manipulation using (i) diffusion-based augmentation that synthesizes geometrically consistent images and action labels to reduce demonstration requirements and (ii) Vision-Language Models (VLMs) that inject high-level semantics for contact-rich motion planning in clutter. We will also introduce ManipBench, which evaluates VLMs’ abilities for low-level manipulation. Together, we show how to move the community towards achieving robot manipulators that can learn and operate with reduced demonstration requirements across cluttered and real-world environments.