Aim
Methods in AI for robotic control, mobile platforms, and cognitive cyber-physical systems are developing rapidly. They tackle the challenging task of modeling real-world systems and environments through data, using machine vision, reinforcement learning for control, probabilistic machine learning, among many others. Such data-driven approaches have led to many concerns regarding the robustness, stability, and overall safety of these systems.

While data-driven approaches based on learning algorithms have seen huge success in the last decade, when applied to cyber-physical systems such as manufacturing applications and healthcare robotics, the lack of safety guarantees causes trust issues. A central challenge is defining and implementing robustness for different applications and providing methods for analyzing and verifying models. This workshop investigates the diverse meaning of robust AI and gathers a wide array of approaches to the problem.

The RAW - workshop provides a forum for bringing together researchers from academia and industry to explore and present their findings in Robust Artificial Intelligence with theories, systems, technologies, and approaches for testing and validating them on challenging real-world, safety-critical applications.




Topics
Interesting research topics for the workshop and for papers include, but are not limited to:

Cognitive models and architectures

Explainable AI

Knowledge-driven models

Safe exploration

Hybrid-models

Reasoning-based methods

Trustworthiness

Understanding and controlling machine learning biases

Adversarial attacks and defense