Enabling Trust in Autonomous Human–Machine Teaming

The advancement of AI enables the evolution of machines from relatively simple automation to completely autonomous systems that augment human capabilities with improved quality and productivity in work and life. The singularity is near! However, humans are still vulnerable. The COVID-19 pandemic reminds us of our limited knowledge about nature. The recent accidents involving Boeing…

Bayesian Emergent Self Awareness

Multisensor signal Data Fusion and Perception, including processing of signals are important cognitive functionalities that can be included in artificial systems to increase their level of autonomy. However, the techniques they rely on have been developed incrementally along time with the underlying assumption that they should have been used mainly to provide a support to…

Information Fusion and Decision Support for Autonomous Systems

In this talk we present our works on decision support analytic for autonomous systems. Decision support analytic process multiple sensory information collected by an autonomous system such as lidar, camera, RGBD, acoustic to perform signal detection, target tracking, object recognition. As multiple sensors are involved, our system uses sensor registration, data association and fusion to…

Improving Manipulation Capabilities of Autonomous Robots

Human-level manipulation continues to be beyond the capabilities of today’s robotic systems. Not only do current industrial robots require significant time to program a specific task, but they lack the flexibility to generalize to other tasks and be robust to changes in the environment. While collaborative robots help to reduce programming effort and improve the…

Morphogenetic Self-organization of Swarm Robots

Self-organization is one of the most important features observed in social, economic, ecological and biological systems. Distributed self-organizing systems are able to generate emergent global behaviors through local interactions between individuals without a centralized control. Such systems are supposed to be robust, self-repairable and highly adaptive. However, design of self-organizing systems is very challenging, particularly…

Sustainable Autonomy: Challenges and Perspectives

Cutting-edge autonomous systems demonstrate outstanding results in many important tasks requiring intelligent data processing under well-known conditions. However, the performance of these systems may drastically deteriorate when the data are perturbed, or the environment dynamically changes, either due to natural or man-made disturbances. The challenges are especially daunting in edge computing scenarios and on-board applications…

Verification, Trustworthiness, and Accountability of Human-Driven Autonomous Systems

Despite the fact that autonomous systems’ science and control theory have almost 50 years of history, the community is facing major challenges to ensure the safety of fully autonomous consumer systems. It mostly concerns the verification and high fidelity operation of safety-critical systems, may that be a self-driving car, a homecare robot or a surgical…

Drone Vision and Deep Learning for Infrastructure Inspection

This lecture overviews the use of drones for infrastructure inspection and maintenance. Various types of inspection, e.g., using visual cameras, LIDAR or thermal cameras are reviewed. Drone vision plays a pivotal role in drone perception/control for infrastructure inspection and maintenance, because: a) it enhances flight safety by drone localization/mapping, obstacle detection and emergency landing detection;…

On Ethics of Autonomous and Intelligent Systems (AI/S)

In the 4th industrial revolution under which autonomous, intelligent systems are designed, human brain capacities are delegated to machines. This brings in great opportunity to reduce the need for human intervention in daily lives, together with considerable ethical challenges. The role of the designers of such systems, i.e., engineers, is most important in balancing the…