Will Poppy Fall? Predicting Robot Falls in Advance Based on Visual Input

Borui He, Garrett E. Katz

Abstract: Falling is a critical problem for both people and robots, which may cause bodily harm to the elderly or prevent robots from executing issued orders. This motivates applications of machine learning to recognize and detect falls. Many datasets have been collected for this purpose, but primarily for detecting human falls after they occur. In this paper, we contribute simulated and real training data for robotic fall prediction in advance, based on egocentric video. We also compare an existing fall recognition model with a custom deep architecture we designed, to establish baseline performance on our datasets. We find that our architecture performs well for various prediction spans that can shift between training and testing.