Verification of Deep Neural Networks and Systems with NN Components
Deep neural networks are widely used for nonlinear function approximation with applications ranging from computer vision to control. Although these networks involve the composition of simple arithmetic operations, it can be very challenging to verify whether a particular network satisfies certain input-output properties. This project classifies methods that have emerged recently for soundly verifying such properties. These methods borrow insights from reachability analysis, optimization, and search. We investigate fundamental differences and connections between existing algorithms. In addition, we provide pedagogical implementations of existing methods and compare them on a set of benchmark problems. Moreover, we will extend these tools to verify closed-loop systems with NN components by combining with control-theoretic analysis.
Micro to Macro Traffic Management and Modeling with Autonomous Vehicles
Automated vehicles are believed to be the key solution for future mobility. As more and more automated vehicles drive on public roads, they will interact with each other, and with other road participants such as human-driven vehicles and pedestrians. Those interactions will deeply change and redefine today’s transportation system.
The fundamental question is: how can we achieve a safe and efficient transportation system through the design of the driving strategies for single automated vehicle? This project is aimed to develop an understanding on how the behavior design of single agent and their communication strategy may affect the overall multi-agent system, and how to achieve the best design from the system perspective. Indeed, the macro transportation system depends on the micro behaviors of road participants, while the micro behaviors of road participants are affected by others in the transportation system. From micro design to macro analysis, we are expected to gain better understandings of the micro-macro relationships and achieve a safe and efficient transportation system through the introduction of automated vehicles.
Safe and Efficient Robot Collaboration System (SERoCS)
In factory automation, humans and robots comprise the two major work forces. Traditionally, humans and robots have not physically collaborated with each other during operation, in significant part because full automation with robots was the goal. In recent years, however, it has been recognized that there are tremendous advantages if robots are brought out of their cages and to allow them to share work space with and to collaborate with humans to take advantage of the best of two worlds - on one hand, the reliable execution of tasks by robots without wear handling objects of a wide range of sizes and weights, and on the other hand, the intelligence of humans and their five senses-based adaptability and flexibility. For collaboration between humans and robots to be successful, it is a prerequisite to ensure the safety of the humans in such collaboration. At the same time, it is important to ensure that robots collaborate with humans to ensure the best performance possible.
Robustly-Safe Automated Driving (ROAD) Systems
Automated driving is widely viewed as a promising technology to revolutionize today’s transportation system, so as to free the human drivers, ease the road congestion and lower the fuel consumption among other benefits. Substantial research efforts are directed into this field from research groups and companies. When the automated vehicles drive on public roads, they are automatically given social attributions. While existing technologies can assure high-fidelity sensing, real-time computation and robust control, the challenges lie in the interactions between the automated vehicle and the environment which includes other manually driven vehicles. We proposed a framework in designing the driving behavior for automated vehicles to prevent or minimize occurrences of collisions among vehicles and obstacles while maintaining efficiency (e.g. maintaining high speed on freeway).
Robot Safe Interaction Systems (RSIS) for Intelligent Industrial Co-Robots
With the development of modern robotics, robots are entering people’s life in multiple ways. As identified by National Robotics Initiative (NRI), future intelligent robots can be co-defenders, co-explorers, co-inhabitants and even co-workers to human. To successfully launch those co-robots, we must make sure that they are safe to human users.
However, this is not a easy task as the robots are operating in a dynamic uncertain environment (DUE) together with other intelligent agents such as humans. In this project, we address the safety issues in the context of (1) multi-agent interactions (2) sensing and knowledge representations (3) learning and predictions (4) human modeling and (5) constrained optimal control and decision-making.
