Faculty of Engineering
ACFR Seminar Series [https://d31hzlhk6di2h5.cloudfront.net/20260501/44/a0/ac/85/83de99ae1318da0ad...] [https://d31hzlhk6di2h5.cloudfront.net/20260501/c6/93/17/4e/93651822f71449ace...] Optimizing for the Unknown: Toward Robots That Can Go Anywhere, Handle Anything, and Learn from Little
Presented by Dr. Ian Abraham Date: Thursday, 7 May 2026, at 1:00 pm AEDT Venue: ACFR seminar area, J04 level 2 (Rose St Buildinghttps://url.au.m.mimecastprotect.com/s/OzZuCJyBrGf1L7EEniYcoUyzu3x?domain=t.e2ma.net) Zoom ID: https://uni-sydney.zoom.us/s/87306457455https://url.au.m.mimecastprotect.com/s/A_lJCK1DvKTBxj551TNf4U5Ca3P?domain=t.e2ma.net
**RSVP by Tuesday 5 May for catering purposes. Accept this calendar invitation if you plan to attend the seminar in person and join us for a light lunch at approximately 12.30pm.
Abstract:
Robots have the potential to become extraordinary tools that extend humanity’s reach, automating tasks that range from exploring the solar system and the vast oceans to manipulating delicate objects in our homes. Across these settings, robots face a common challenge: they must make good decisions when the world is only partially known, data are scarce, and mistakes are costly. In this talk, I will present recent work from my group on principled numerical optimization methods that enable robots to reason, act, and learn in these partially observable, remote, and uncertain worlds. I will show how these methods expand robotic capabilities to support long-horizon ocean exploration by balancing information gathering, safety, and resource constraints; improve precision manipulation by accounting for uncertainty in perception, contact, and dynamics; and accelerate multi-modal (visual) policy learning by extracting more capability from limited experience. Across these domains, the central theme is that optimization provides a powerful language for building robots that are not merely reactive, but purposeful robots that can plan over long timescales, adapt to changing conditions, and learn efficiently from the data available to them. Together, these methods point toward a future in which robots can go farther, handle more varied tasks, and learn faster in the real world—bringing robust autonomy to environments where human presence is limited, uncertainty is unavoidable, and intelligent decision-making matters most.
Bio:
Ian Abraham is currently a Senior Lecturer in the School of Electrical and Computer Engineering at the University of Sydney with an affiliation at the Australian Centre for Robotics. His research sits at the intersection of robotics, optimization, control, machine learning, and artificial intelligence. His work develops computational methods that enable robotic systems to intelligently interact with, explore, and learn in extreme and remote environments. He has been recognized with numerous prestigious awards, including an NSF CAREER award as a professor in the US. Prior to joining the University of Sydney, Ian was an Assistant Professor at Yale University in the Mechanical Engineering and Computer Science Departments. He received his PhD degree from Northwestern University and was a Postdoctoral Scholar at the Robotics Institute at Carnegie Mellon University.
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