It is quite challenging to ensure the safety of reinforcement learning (RL) agents in an
unknown and stochastic environment under hard constraints that require the system state …

The robustness of deep neural networks has received significant interest recently, especially
when being deployed in safety-critical systems, as it is important to analyze how sensitive …

Neural networks have shown great promises in planning, control, and general decision
making for learning-enabled cyber-physical systems (LE-CPSs), especially in improving …

Neural networks (NNs) playing the role of controllers have demonstrated impressive
empirical performance on challenging control problems. However, the potential adoption of …

Future autonomous systems will employ complex sensing, computation, and communication
components for their perception, planning, control, and coordination, and could operate in …

The rapid advancement of edge and cloud computing platforms, vehicular ad-hoc networks,
and machine learning techniques have brought both opportunities and challenges for next …

Future autonomous systems will employ sophisticated machine learning techniques for the
sensing and perception of the surroundings and the making corresponding decisions for …

In the current control design of safety-critical cyber-physical systems, formal verification
techniques are typically applied after the controller is designed to evaluate whether the …

Model-based reinforcement learning has been widely studied for controller synthesis in
cyber-physical systems (CPSs). In particular, for safety-critical CPSs, it is important to …

This letter presents a novel design of a soft tactile finger with omni-directional adaptation
using multi-channel optical fibers for rigid-soft interactive grasping. Machine learning …