Medical robotics includes a number of devices used for surgery, medical training, rehabilitation therapy, prosthetics, and assisting people with disabilities. Nowadays, robotic devices are used to replace missing limbs, perform delicate surgical procedures, deliver neurorehabilitation therapy to stroke patients, teach children with learning disabilities, and perform a growing number of other health related tasks. The most extensive use of robotic technology for medical applications is in rehabilitation robotics, which traditionally includes assistive robots, prosthetics, orthotics, and therapeutic robots. Moreover, in the last decade, surgery and robotics have reached a maturity that has allowed them to be safely assimilated to create a new kind of operating room. This new environment includes robots for local surgery and telesurgery, audiovisual telecommunication for telemedicine and teleconsultation, robotic systems with integrated imaging for computer-enhanced surgery, and virtual reality (VR) simulators enhanced with haptic feedback, for surgical training.
However, usually concrete models and specific knowledge are not available for objects or events in the medical robot's work environment. Thus, a medical robot system has to rely on more generalized modes of inference to infer the semantic content of the context. Adequate models and knowledge may then describe broad categories of objects or events, acquired through training on sets of numerous examples. Knowledge may also be inferred from similarities and correspondences discovered between novel and known cases. As a matter of facts, there is a growing tendency to introduce high-level semantic knowledge into robotic systems.
When a medical robot encounters unknown objects in its environment and semantic models are available, the perceptual system can derive knowledge from the relationships established with known objects of a similar typology. Moreover, through semantic modeling of low level features within a scenario, robots can generate representation of such features in a level of abstraction where logical reasoning methods could be applied for decision making. Furthermore, at such semantic level more than one modalities can be merged to complement each other and produce logical inferences.
As a result, different cognitive systems have become quite popular among the research community, specially those using deep learning techniques over images and language sources, showing promising results.
Thus, this workshop provides a uniquely focused forum for the discussion of the intersection of different research areas, such as audio, speech, language, images and some others into unique medical robotics systems that can auto-improve by learning and can be exploited through different reasoning techniques.
This workshop will bring together the foremost researchers from different fields of robotics sharing and unifying techniques that can be applied to different medical areas where they are currently used, such as surgery, rehabilitation, simulation, and so on.
Eventually, we encourage papers reporting novel contributions related to the creation, representation, and use of semantic knowledge in medical robots which present techniques that have been experimentally validated on real medical robotic systems.
Topics of interest include, but are not limited to:
- Semantic medical robot vision
- Semantic scene interpretation
- Cooperative human-robot interaction and task solving
- Object modeling
- Deep learning for perception, action, and control in medical robotic systems
- Use of semantic information in mapping or knowledge acquisition
- Multimodal knowledge representation in robotic medical systems
- Semantic modeling of multimodal feature space
- Multimodal fusion at semantic level
- Heterogeneous cognitive medical robotics systems
- Deep semantic processing of heterogeneous information
- Medical robot navigation framework
- Semantic techniques applied to medical robots