This is a Munich news story, published by HUB, that relates primarily to Johns Hopkins University news.
For more Munich news, you can click here:
more Munich newsFor more medical innovations news, you can click here:
more medical innovations newsFor more news from HUB, you can click here:
more news from HUBOtherweb, Inc is a public benefit corporation, dedicated to improving the quality of news people consume. We are non-partisan, junk-free, and ad-free. We use artificial intelligence (AI) to remove junk from your news feed, and allow you to select the best health news, business news, entertainment news, and much more. If you like medical innovations news, you might also like this article about
surgical robots. We are dedicated to bringing you the highest-quality news, junk-free and ad-free, about your favorite topics. Please come every day to read the latest robotic surgery news, da Vinci Surgical System robot news, medical innovations news, and other high-quality news about any topic that interests you. We are working hard to create the best news aggregator on the web, and to put you in control of your news feed - whether you choose to read the latest news through our website, our news app, or our daily newsletter - all free!
medical roboticsHUB
•
88% Informative
Johns Hopkins University researchers used imitation learning to train the da Vinci Surgical System robot to perform three fundamental tasks required in surgical procedures.
In each case, the robot trained on the team's model performed the same surgical procedures as skillfully as human doctors.
The findings are being spotlighted this week at the Conference on Robot Learning in Munich , a top event for robotics and machine learning.
VR Score
93
Informative language
95
Neutral language
49
Article tone
semi-formal
Language
English
Language complexity
57
Offensive language
not offensive
Hate speech
not hateful
Attention-grabbing headline
not detected
Known propaganda techniques
not detected
Time-value
long-living
External references
3
Source diversity
3
Affiliate links
no affiliate links