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Driven by the rapid progress in mobile sensing and computing, wearable computing has developed powerful methods for the automatic recognition, categorization, and labeling of human actions and behaviors from sensor data. Because of the stringent requirements dictated by user acceptance, these methods are typically robust to human variability and hardware-dependent factors, including variability in sensor type and placement. This makes them a potentially useful tool for the automatic recognition and labeling of robot behaviors and may lead to new opportunities for research in robotics. We detail three domains in which the methods of activity recognition can play a role in robotics.