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About this paper
Pengfei Yi, Yifan Han, Junyan Li, Litao Liu, Wenzhao Lian
MAE-Select lets a robot choose among discrete camera viewpoints during manipulation. It trains a multi-view masked autoencoder to estimate features from one selected view, then uses future action-prediction error to train the next-view selector. On simulated Put Box In Cabinet, it reaches 50% success versus 42% for the best fixed-view baseline. Training conditions differ, and continuous viewpoint optimization remains unsolved.
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