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.
Loco-manipulation, which requires mobile manipulators to coordinate locomotion and manipulation over long horizons, is a key capability for large-object rearrangement in applications such as service robotics and warehouse automation. Despite recent progress, reliably executing these tasks remains challenging because performance depends on complex robot-object and object-environment interaction dynamics. Existing approaches address this challenge either through model-based methods, which often require accurate dynamics models, or through learning-based methods, which reduce this requirement but typically assume a fixed or predefined initial grasp. However, different geometrically stable grasps can lead to substantially different downstream manipulation performance. Therefore, we propose Grasp-TAGS, a task-aware grasp selection framework for goal-conditioned large-object rearrangement. We train a unified PPO-based reinforcement learning policy across multiple initial grasp configurations and distill the learned critic into a lightweight grasp utility network that ranks candidate grasps using only the candidate grasp configuration and goal information. At inference time, we select the grasp with the highest predicted downstream utility for the downstream policy to complete the task, before even making a contact. Experiments demonstrate that Grasp-TAGS improves rearrangement success over both fixed single-grasp policies and random grasp selection, achieving an average success rate of 89.84% compared with 79.87% for the best-performing single-grasp policies, an improvement of 9.97 percentage points. Grasp-TAGS also reduces final orientation errors across all evaluated objects. For more qualitative results, refer to the supplementary webpage: https://rpm-lab-umn.github.io/Grasp-TAGS-web-page/