AUTHOR=Hulin Thomas , Panzirsch Michael , Singh Harsimran , Coelho Andre , Balachandran Ribin , Pereira Aaron , Weber Bernhard M. , Bechtel Nicolai , Riecke Cornelia , Brunner Bernhard , Lii Neal Y. , Klodmann Julian , Hellings Anja , Hagmann Katharina , Quere Gabriel , Bauer Adrian S. , Sierotowicz Marek , Lampariello Roberto , Vogel Jörn , Dietrich Alexander , Leidner Daniel , Ott Christian , Hirzinger Gerd , Albu-Schäffer Alin TITLE=Model-Augmented Haptic Telemanipulation: Concept, Retrospective Overview, and Current Use Cases JOURNAL=Frontiers in Robotics and AI VOLUME=8 YEAR=2021 URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2021.611251 DOI=10.3389/frobt.2021.611251 ISSN=2296-9144 ABSTRACT=

Certain telerobotic applications, including telerobotics in space, pose particularly demanding challenges to both technology and humans. Traditional bilateral telemanipulation approaches often cannot be used in such applications due to technical and physical limitations such as long and varying delays, packet loss, and limited bandwidth, as well as high reliability, precision, and task duration requirements. In order to close this gap, we research model-augmented haptic telemanipulation (MATM) that uses two kinds of models: a remote model that enables shared autonomous functionality of the teleoperated robot, and a local model that aims to generate assistive augmented haptic feedback for the human operator. Several technological methods that form the backbone of the MATM approach have already been successfully demonstrated in accomplished telerobotic space missions. On this basis, we have applied our approach in more recent research to applications in the fields of orbital robotics, telesurgery, caregiving, and telenavigation. In the course of this work, we have advanced specific aspects of the approach that were of particular importance for each respective application, especially shared autonomy, and haptic augmentation. This overview paper discusses the MATM approach in detail, presents the latest research results of the various technologies encompassed within this approach, provides a retrospective of DLR's telerobotic space missions, demonstrates the broad application potential of MATM based on the aforementioned use cases, and outlines lessons learned and open challenges.