Robotics and AI are experiencing a radical growth, fueled by innovations in data-driven learning paradigms coupled with novel device design, in applications such as healthcare, manufacturing and service robotics. The challenges of advanced robotics and intelligent decision cannot be addressed by developing new learning algorithms or designs in isolation. The aim of my research is to bridge this gap, and enable generalization in robotic systems as we move towards human skill augmentation. I am interested enabling learning from imprecise information for performing a range of tasks with independence and flexibility. I will develop new mathematical models for data-driven optimization, coupling design of physical systems with algorithms and building new formalisms for learning based autonomy.
In this talk, I will present three specific instances of my research in robotics for interventional healthcare spanning these three areas: algorithmic design, optimization and learning from demonstrations. I will first describe how algorithmic design can improve personalized delivery of radiation therapy and enable task-level autonomy in minimally invasive surgery. Then I will discuss algorithmic paradigm towards enabling complex multi-step sequential tasks. Finally, I'll discuss motivations and methods in transferring learned controllers across dynamics models from simulation to real robots. While the algorithms and techniques introduced are applicable across domains in robotics; in this talk, I will exemplify these ideas through my work on medical robotics.
Animesh Garg is a Postdoctoral Researcher at Stanford University AI lab. Animesh is interested in problems at the intersection of optimization, machine learning, and design. He studies the interaction of data-driven Learning for autonomy and Design for automation for human skill-augmentation and decision support. Animesh received his Ph.D. from University of California, Berkeley where he was a part of the Berkeley AI Research center and the the Automation Science Lab. His research has been recognized with Best Applications Paper Award at IEEE CASE, Best Video at Hamlyn Symposium on Surgical Robotics, and Best Paper Nomination at IEEE ICRA 2015. And his work has also featured in press outlets such as New York Times, UC Health, UC CITRIS News, and BBC Click.