Identifying Appropriate Machine Learning Models for Multi Robot Secure Coordination in a Healthcare Facility
Student Researcher: Shota Nemoto
Mentor: Debbie Perouli
In the near future, the U.S. will experience a severe shortage of Registered Nurses. A proposed solution is the development of Robotic Caregivers (RCGs), both service and social robots, which will be able to provide care autonomously. Commercial service robots that are currently available, such as Temi and Loomo, provide APIs for developers to create applications for these RCGs. Many applications will input sensor data into machine learning models, which may leave it vulnerable to attack from an adversary attempting to retrieve a patient’s personal data or fool a model into mislabeling or misclassifying an input.
The objective of this project is to research an adversarial attack on these robots.
Milestones and Goals
|2: Initial Reading||
|3: Form Research Hypothesis||
|4: Design Experiements and Methodology||
|5: Begin Poster and Paper Creation||
|6: Implement System||
|7: Run Experiments||