Identifying Appropriate Machine Learning Models for Multi Robot Secure Coordination in a Healthcare Facility

From REU@MU
Jump to: navigation, search

Student Researcher: Shota Nemoto

Mentor: Dr. Debbie Perouli

Project Description

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

Week Description
1: Orientation
  • Meet other REU students and mentors
  • Learn basic data science concepts
2: Initial Reading
  • Investigate API for Temi and Loomo robots.
  • Learn about adversarial networks and potential attacks by looking at recent conferences, workshops, and journals published.
  • Find a specific adversarial attack to research
3: Form Research Hypothesis
  • Investigate deeper into selected adversarial attack
  • Form research hypothesis
4: Design Experiements and Methodology
  • Recreate L-BFGS-B method for finding adversarial examples
  • Find other potential optimization methods for generating adversarial examples
5: Begin Poster and Paper Creation
  • Present Current Progress
6: Implement System
  • TBD
7: Run Experiments
  • TBD
8: Evaluation
  • Evaluate research on adversarial attack
  • Prepare report on results
9: Finalize Poster and Paper
  • Create graphics for poster and paper
  • Write descriptions of experiments and methods
10: Present Research
  • Finish / Polish final poster and paper
  • Present Poster