Associate Professor Oliver Obst
Associate Professor
Engineering
Computer Science and Engineering
Oliver Obst is an Associate Professor in Robotics in the School of Computer Science and Engineering at UNSW Sydney. His research focuses on embodied intelligence and collective robotics, with an emphasis on efficient learning systems, recurrent neural networks, and autonomous platforms operating in real-world environments.
He develops methods for learning and decision-making under uncertainty. His work covers multi-robot coordination, decentralised perception, and long-horizon autonomy, with applications in health and intelligent infrastructure.
He has held senior academic leadership roles at Western Sydney University, including Director of a research centre and Head of Department, and previously worked at CSIRO/Data61 as a Research Team Leader. He served on the board of trustees of the RoboCup Federation and mentors student robotics teams. He holds a PhD in Artificial Intelligence from the University of Koblenz–Landau, Germany.
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J17 level 5
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Digital twin for the manufacture of composite structures by resin infusion (ARC DP260103722)
2026–2029. ARC Discovery Project (administered by Western Sydney University). Lead CI: Prof Brian Falzon (WSU). CIs: A/Prof Oliver Obst (UNSW) and Prof Liyong Tong (University of Sydney). This project develops a digital twin framework for resin infusion, integrating high-fidelity modelling with machine learning to predict defects in real time and support adaptive process control. The goal is improved manufacturing reliability and throughput with reduced material waste, with relevance to lightweight composite structures used in aerospace and renewable energy.
We explore how robots can learn, plan, and act effectively in the physical world, individually and collectively.
Our research focuses on:
- data-efficient embodied learning and manipulation
- collective and multi-robot systems operating under uncertainty
- learning and inference methods for physical systems
We are interested in approaches that combine learning, probabilistic reasoning, and real-world experimentation, with an emphasis on efficiency, robustness, and physical grounding.