Real-time Human Presence Estimation For Indoor Robots

Apaar Sadhwani

Abstract: As robots become more versatile, fostering effective interactions with humans within shared spaces becomes paramount. This paper introduces the notion of real-time human presence estimation for indoor robots. Real-Time Presence (RTP) asks the question, “Where in the home is the human now?”, which is an important primitive for home robots. Its answer is challenging and inherently probabilistic as the robot only observes humans sparsely through the day. The mobility of the robot adds further complexity. Conventional state estimation approaches can be adapted to handle sparsity and even mobility. However, they fail to leverage the diverse contextual cues of a home environment. We present a novel machine learning approach for RTP that is additionally scalable to diverse home signals. Trained using curriculum learning, the model incorporates both time-based and event-based signals. Experimental results demonstrate the model’s proficiency in understanding floorplan topology and human behaviors.