Abstract: Automating medical interventions, such as surgery, through robots holds immense potential to revolutionize healthcare delivery by alleviating physician workload and extending critical treatments to underserved populations. Successful automation of medical interventions demands robots capable of three essential abilities: environment understanding with high precision (sensing), reliable manipulation in medical environments that guarantees patient safety and minimizes failures (planning), and continuous medical knowledge accumulation to operate in diverse clinical scenarios (adaptability). However, the complexity of medical environments, restricted sensor feedback, and stringent safety constraints pose challenges in developing autonomous robotic systems that match human professionals' expertise. 

In this talk, I will introduce our research in robot sensing, planning, and adaptability that achieves precise localization, safe manipulation, and flexible learning in autonomous medical interventions. First, I will discuss our approach to surgical tool localization that leverages robot kinematics and object geometry to handle uncertainty while ensuring feasibility constraints. Second, I will present how our uncertainty-aware trajectory optimization framework generates reliable robot movements for surgical manipulation, even in noisy, unpredictable environments. Next, I will highlight our efforts in robot learning that enhance knowledge accumulation across multiple surgical and general manipulation tasks, improving learning efficiency and adaptability. Finally, I will demonstrate the real-world impact of our research by showcasing two autonomous medical applications: suturing, a fundamental surgical procedure, and human repositioning for medical evacuation. This talk will conclude with promising future directions in autonomous medical interventions.

Bio: Zih-Yun (Sarah) Chiu is a Ph.D. candidate in Electrical and Computer Engineering at the University of California San Diego (UCSD). She works with Professor Michael Yip in the Advanced Robotics and Controls Lab. Her research interests lie in high-precision robot autonomy for medical applications, including surgery and human evacuation in search-and-rescue scenarios. She has developed localization, planning, and robot learning techniques that enable robots to achieve precise perception, safe manipulation, and efficient learning in complex medical environments. Her work has been recognized with the 2023 ICRA Best Paper Award in Healthcare and Medical Robotics and the Best Poster Award at the 2023 ICRA Workshop on New Evolutions in Surgical Robotics. In 2024, she was honored as a Rising Star in EECS.