Multimodal and Force-Matched Imitation Learning with a See-Through Visuotactile Sensor

arXiv:2311.01248v3 Announce Type: replace-cross
Abstract: Contact-rich tasks continue to present a variety of challenges for robotic manipulation. In this work, we leverage a multimodal visuotactile sensor within the framework of imitation learning (IL) to perform contact rich tasks that involve relative motion (slipping/sliding) between the end-effector and object. We introduce two algorithmic contributions, tactile force matching and learned mode switching, as complimentary methods for improving IL. Tactile force matching enhances kinesthetic teaching by reading approximate forces during the demonstration and …

abstract arxiv challenges cs.ai cs.lg cs.ro framework imitation learning manipulation multimodal object replace robotic robotic manipulation sensor tasks the end through type work