Adaptive motion generation using imitation learning and highly compliant end effector for autonomous cleaning

Recent demographic trends in super aging societies, such as Japan, is leading to severe worker shortage. Service robots can play a promising role to augment human workers for performing various household and assistive tasks. Toilet cleanup is one such challenging task that involves performing complaint motion planning in a constrained toilet setting. In this study, we propose an end-to-end robotic framework to perform various tasks related to toilet cleanup. Our key contributions include the design of a complaint and multipurpose end-effector, an adaptive motion generation algorithm, and an autonomous mobile manipulator capable of garbage detection, garbage disposal and liquid removal. We evaluate the performance of our framework with the competition setting used for toilet cleanup in the Future Convenience Store Challenge at the World Robot Summit 2018. We demonstrate that our proposed framework is capable of successfully completing all the tasks of the competition within the time limit.