Robots that interact with real people in real-world environments must adapt to diverse human preferences, tasks, and constraints, yet achieving this remains a significant challenge. While current approaches rely on collecting massive datasets to build generalizable models, this strategy is expensive, slow, and often brittle. Instead, robots must learn to adapt efficiently, learning "a lot from a little" human input. In this talk, I argue that robots don’t just need more data—they need better data. To learn a lot from a little, we need to rethink how we engage with human input and the structures we build around it. By designing information rich yet effortless human inputs, amplifying sparse data through simulation and LLM priors, and structuring the robot learning problem with strong behavioral abstractions, we can create robots that learn efficiently and align with human needs in diverse environments. This shift from data quantity to data quality represents a key step toward human-aligned robot learning for real-world applications.