Recent advances in machine learning models allowed robots to identify objects on a
perceptual nonsymbolic level (eg, through sensor fusion and natural language …

Surgical robot automation has attracted increasing research interest over the past decade,
expecting its potential to benefit surgeons, nurses and patients. Recently, the learning …

Given a natural language instruction and an input scene, our goal is to train a model to
output a manipulation program that can be executed by the robot. Prior approaches for this …

Robot-assisted minimally invasive surgery is improving surgeon performance and patient
outcomes. This innovation is also turning what has been a subjective practice into motion …

Purpose Multi-and cross-modal learning consolidates information from multiple data sources
which may offer a holistic representation of complex scenarios. Cross-modal learning is …

The proliferation of robot-assisted minimally invasive surgery highlights the need for
advanced training tools such as cost-effective robotic endotrainers. Current surgical robots …

Adoption of robotic-assisted surgery has steadily increased as it improves the surgeon's
dexterity and visualization. Despite these advantages, the success of a robotic procedure is …

In the future, surgical robots will grant the option of executing surgical tasks autonomously,
supervised by the surgeon. We propose a simple framework for learning surgical action …

Context As robot-assisted surgery is increasingly used in surgical care, the engineering
research effort towards surgical automation has also increased significantly. Automation …

Purpose Automatically segmenting and classifying surgical activities is an important
prerequisite to providing automated, targeted assessment and feedback during surgical …