Breaking and Fixing Autonomous Cyber-Physical Tactical Systems
Abstract
Autonomous systems, including self-driving cars and air vehicles, have caught the imagination of the press and the public. However, broader adoption of such systems in safety-critical applications has been the subject of intense debate and scrutiny. The stunning performance of deep learners compared to extant methods, including pattern matching, statistical methods, and legacy machine-learning algorithms, has taken the research world by storm. This has naturally led the U.S. Department of Defense (DoD) community to ask, "How do we harness this technology being unleashed upon the world?" Before we answer this question, however, it is important to note that trust is integral to DoD applications, including autonomous systems, and ensuring reliable system operations is paramount. Therefore, we need strategies that harness deep-learning algorithms to provide the DoD with autonomous systems that are robust, secure, timely, and dependable. In this webinar, we will investigate issues leading to poor generalization and lack of robustness of autonomous systems based on machine learning and discuss the state of the art in their mitigation.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 11, 2021
- Accession Number
- AD1125859
Entities
People
- Ramesh Bharadwaj
Organizations
- United States Naval Research Laboratory