For remote telesurgery, the bandwidth requirement can be reduced by transferring atomic surgical actions (“surgemes") instead of the low-level kinematic information. However, such a policy requires accurate recognition of these surgemes. Surgeme recognition requires the surgical data for training the classifier. In this paper, we employ transfer learning to reduce the data requirement by transferring knowledge from training task (peg-transfer) to a surgical task (debridement). With different ablations we demonstrate that tranfer learning requires 40% less data than with no-transfer.

Dexterous Skill Transfer between Surgical Procedures for Teleoperated Robotic Surgery

In this work, we have developed an autonomous robotic assistant for the task of blood suction in robotic surgery. This system was demonstrated to improve surgical performance and reduced cognitive demands compared to the manual teleoperation of the suction. Moreover, the system was tested in a user study. In this, the autonomy collaborated with the user to complete a surgical task. This study allowed us to identify key collaboration aspects required for an effective autonomous blood suction system.

Multi-camera Torso Pose Estimation using Graph Neural Networks

Skill imitation has been an important ability in
human-robot collaboration since it allows expeditious robot
teaching of new tasks. To mimic the human
pose, inverse kinematic solvers have been used to endow kinematic
structures with human-like motion. Nevertheless, these
solutions tend to be formulated for a specific robot or task. To
address this generalization issue, this work presents the ICONS
framework for imitation constraints, which proposes a general
formulation for pose imitation, paired with a computationally
efficient solver.

ICONS: Imitation CONStraints for Robot Collaboration

Interactive 4A - Applications of Social Robots 3

technical paper

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. This theme also touches upon people's expectations and perceptions of robots, as well as other social, cognitive and cultural factors that will influence human-robot interactions. We will have a special session/track on this topic, invite targeted submissions and include plenary speakers who are specialist in this area. We sincerely hope that this focus will help enlighten the design-thinking of all of us as we fabricate and program our own various systems.

To access RO-MAN Virtual 2021, [please register here.](https://ro-man2021.org/registration/)

IEEE RO-MAN 2021

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. 

Juan Wachs

3

Presentations

Dexterous Skill Transfer between Surgical Procedures for Teleoperated Robotic Surgery

Multi-camera Torso Pose Estimation using Graph Neural Networks

ICONS: Imitation CONStraints for Robot Collaboration

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