Robot Learning to Mop Like Humans Using Video Demonstrations

Though mopping the floor is a mundane and tedious daily task, enabling robots to perform it comparably to humans remains a challenge. Hand-coding desired mopping behaviors for variable surfaces and situations is particularly difficult. In this paper, we develop a robotic system for mopping the floor by mimicking the human behavior demonstrated in videos. Our baseline robotic system uses traditional computer vision techniques for tracking and inverse kinematics. Our proposed robot mop learning system comprises advanced computer vision techniques, Time Contrastive Network (TCN), and reinforcement learning. Using these, we devise a reward function for the mopping task. We use a Universal 10e robotic arm attached to a mop to perform the mopping task and a first-person camera attached on top of the robotic arm to provide feedback for robotic learning. We evaluate our proposed robot mop learning system's imitative similarity using optical flow, distance in mop location, and force applied to the floor, as well as cleaning efficiency using a white glove method.