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Learning to Predict Humanoid Fall

Falls are undesirable in humanoid robots, but also inevitable, especially as robots get deployed in physically interactive human environments. We consider the problem of fall prediction: to predict if the balance controller of a robot can prevent a fall from the robot's current state. A trigger from the fall predictor is used to switch the robot from a balance maintenance mode to a fall control mode. It is desirable for the fall predictor to signal imminent falls with sufficient lead time before the actual fall, while minimizing false alarms. Analytical techniques and intuitive rules fail to satisfy these competing objectives on a large robot that is subjected to strong disturbances and exhibits complex dynamics. We contribute a novel approach to engineer fall data such that existing supervised learning methods can be exploited to achieve reliable prediction. Our method provides parameters to control the tradeoff between the false positive rate and the lead time. Several combinations of parameters yield solutions that improve both the false positive rate and the lead time of hand-coded solutions. Learned solutions are decision lists with typical depths of 5–10, in a 16-dimensional feature space. Experiments are carried out in simulation on an ASIMO-like robot.