Real-world robot imitation learning is often limited by the cost of collecting physical demonstrations, which typically yield only one action label per visited state. We present CrowdMAPS, a web-based crowdsourcing paradigm for augmenting real-robot datasets with Multiple Actions per State (MAPS). After or during standard robot data collection, CrowdMAPS distributes visited robot states to remote annotators through a web-based 3D interface, where they specify alternative valid actions without requiring access to physical robot hardware. This expands robot datasets along a complementary "width" dimension--by increasing the number of actions per visited state--rather than only by collecting more trajectories. We evaluate CrowdMAPS using keyframe-centric goal-pose annotations supplied by non-expert annotators and train imitation policies on the resulting MAPS data. Across five real-world manipulation tasks, increasing MAPS width from one to ten actions per state improves sample efficiency and increases average success rates by 35.6 percentage points for keypose policies and 33.0 percentage points for trajectory policies. These results show that software-mediated human annotations can augment physical robot data to afford denser supervision, establishing CrowdMAPS as a scalable digital layer for real-robot data collection.

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