Recent work on sensing-aided beam prediction has shown initial promising results in utilizing the sensory data such as RGB images, LiDAR, radar and GPS positions for the beam prediction problem. To take these interesting ideas one step towards real-world deployments, we need to answer the following important questions: (i) Can sensing-aided beam prediction solutions perform well on real-world data? and (ii) Can the developed ML models that are trained on certain scenarios generalize their beam prediction/learning to new scenarios that they have not seen before?