New adversarial ML applications on safety-critical human-robot systems

In this talk, I will discuss several applications of adversarial ML to enhance safety of human-robot systems. All the applications are under a general framework of minimax optimization over neural networks, where the inner loop computes the worst case performance and the outer loop optimize NN parameters to improve the worst case performance. We have applied this approach to develop robust models for human prediction, to learn safety certificate for robot control, and to jointly synthesize robot policy and the safety certificate.

Bio: Dr. Changliu Liu is an assistant professor in the Robotics Institute, School of Computer Science, Carnegie Mellon University (CMU), where she leads the Intelligent Control Lab. Prior to joining CMU, Dr. Liu was a postdoc at Stanford Intelligent Systems Laboratory. She received her Ph.D. from University of California at Berkeley and her bachelor degrees from Tsinghua University. Her research interests lie in the design and verification of intelligent systems with applications to manufacturing and transportation. She published the book “Designing robot behavior in human-robot interactions” with CRC Press in 2019. She initiated and has been organizing the international verification of neural network competition (VNN-COMP) since 2020. Her work is recognized by NSF Career Award, Amazon Research Award, and Ford URP Award.