When: Thurs, Oct 10th 2019, 4:00p
Where: Rose St Building Seminar Area
Title: Human/animal motion prediction and robotic path planning in dynamic environments
Abstract: Fully autonomous and safe operations of intelligent platforms relies on an accurate understanding and prediction of the changing environment. This is especially true for scenarios such as campus, human-robot shared workspace, etc., where, humans behaviours change dynamically. As such, anticipation of human motion and planning paths accounting for such predictions become important yet challenging problems. The former topics are receiving increasingly more attention in recent years. This is a focused session on human/animal motion prediction and robotic path planning in dynamic environments such as crowds or traffic.
Bios: Kunming Li is in the first year of PhD in ITS team, ACFR. He graduated from ANU and worked as a research assistant in Data61 before. He is interested in computer vision and deep learning. In the seminar, he will briefly talk about his phd projects and current research progress. His PhD project is to enable vehicles to have the ability to interact with pedestrians safely and efficiently. Currently, he is exploring to utilize GAN, which was recently proposed and widely used in many learning-based algorithms, to predict pedestrian trajectory.
Weiming (William) Zhi is a PhD candidate with the School of Computer Science, supervised by Professor Fabio Ramos. He received his Bachelors of Engineering (Honours) at the University of Auckland, with a focus on mathematical optimisation and operations research. His honours thesis explores alternative market clearing mechanisms in energy markets. His current research interests lie in utilising machine learning methods to obtain robust solutions in robotics problems, with specific interests in probabilistic methods for motion prediction, and robust motion planning methods in dynamic environments.
Stuart Eiffert joined the ACFR in 2018 and is currently completing a PhD within the Agriculture group, supervised by Salah Sukkarieh. He has previously worked as a Research Associate at the Centre for Autonomous Systems at UTS and a research engineer at Laing O’Rourkes R&D group, developing distributed human-robot interaction systems for use in construction and public transport. His current work focuses on robotic motion planning in dynamic environments, where he is extending trajectory prediction models to learn the response of a crowd to a robot’s planned action.