Faculty of Engineering
ACFR Seminar Series [https://d31hzlhk6di2h5.cloudfront.net/20260720/44/a0/ac/85/83de99ae1318da0ad...] [https://d31hzlhk6di2h5.cloudfront.net/20260720/90/62/a1/a0/e806283be685b4d06...]
1) Symmetry and Beyond: Towards Efficient, Generalizable Learning for Low-Level Control of Aerial and Space Robots. 2) Advancing Healthcare Through Soft Robotics and Intelligent Systems
Presented by Asst. Professor Jake Welde (Cornell) & Asst. Professor Thai Mai Thanh (VinUniversity) Date: Thursday, 23 July 2026, at 10:00 am AEST Venue: ACFR seminar area, J04 level 2 (Rose St Buildinghttps://url.au.m.mimecastprotect.com/s/BchzCGv0oyC0RPXLltLcPfB2L8G?domain=t.e2ma.net) Zoom ID: https://uni-sydney.zoom.us/s/87306457455https://url.au.m.mimecastprotect.com/s/L5lXCJyBrGf1v2MQzUWf9fyp_wV?domain=t.e2ma.net
**RSVP by COB Tuesday 21 July 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:
Symmetry and Beyond: Towards Efficient, Generalizable Learning for Low-Level Control of Aerial and Space Robots
To perform useful, interactive work, aerial and space robots must evolve beyond simple, single-body designs (e.g., quadrotors) towards high-dimensional, articulated morphologies. However, explicit analytical controllers for such systems are not easily crafted, and numerical optimal control algorithms quickly overwhelm the lightweight processors available onboard. Reinforcement learning frontloads these burdens to offline training, typically employing vast computational resources to run brittle training pipelines fine-tuned to particular tasks. To mitigate these downsides, we exploit the natural symmetries of robotic systems to efficiently learn high-performance, generalizable policies for low-level robot control. In particular, we develop a theory of symmetry reduction for tracking control problems, proving that a tracking controller trained in a reduced setting will perform equally well on the original system while also generalizing automatically to unseen trajectories. However, the classical notion of symmetry is too rigid to apply to many practical systems, which may enjoy only a very small symmetry group. We thus propose a relaxed notion of symmetry (termed “weak invariance”) that balances structure with generality, enabling us to factor out a much larger symmetry group while still attaining the same performance guarantees, greatly expanding the applicability and impact of these methods.
Advancing Healthcare Through Soft Robotics and Intelligent Systems
This talk presents recent advances in soft robotics and intelligent systems aimed at transforming healthcare and improving the quality of life. It highlights the development of flexible, bio-inspired robotic technologies for minimally invasive surgery, including soft robotic endoscopic platforms integrated with in situ 3D bioprinting and real-time navigation. The talk also explores wearable soft sensors and robotic devices for rehabilitation, enabling low-cost, accessible, and data-driven patient recovery. By combining soft materials, embedded systems, and artificial intelligence, these innovations offer safer, more adaptive, and patient-centered solutions. The presentation concludes with a vision for deploying such technologies in resource-constrained settings to address global healthcare challenges and enhance quality of life at scale.
Bio:
Jake Welde
Jake Welde is an Assistant Professor with the Sibley School of Mechanical and Aerospace Engineering at Cornell University, where he leads the Geometry, Design, and Control Laboratory (or the “GeoDesiC Lab”). Prior to joining Cornell, he spent a decade at the University of Pennsylvania, where he completed his undergraduate and doctoral degrees in Mechanical Engineering and Applied Mechanics and his Masters in Robotics, working in the GRASP Laboratory with Vijay Kumar. He is broadly interested in how mathematical structure — symmetry, hierarchy, and mechanics — can be harnessed to create more capable algorithms for robot control, and how the interplay between control and morphology mediates overall robot capabilities.
Thai Mai Thanh
Dr. Thai Mai Thanh is an Assistant Professor of Mechanical Engineering at VinUniversity and Principal Investigator (PI) of the VinUni Biorobotics Lab. He earned his B.E. from Ho Chi Minh City University of Technology (2016), M.S. from KAIST (2019), and Ph.D. in Biomedical Engineering from UNSW Sydney (2023), where he also completed a postdoctoral fellowship in medical robotics.
His research focuses on advancing healthcare through soft robotics, intelligent systems, and medical devices, with applications in minimally invasive surgery, in situ bioprinting, rehabilitation, and wearable haptics. He has authored over 40 publications and holds multiple international patents, with a strong emphasis on translating cutting-edge technologies into real-world clinical impact.
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