Title: Computational Methods for Developing Agile and Intelligent Robots
Abstract: Recent advances in both software and hardware opened a new horizon of robotics: artificial intelligence discovered dynamic motions in simulation and hardware became powerful enough to execute human-level stunts. However, the current state-of-the-art robots are yet far from operating in the real world due to lack of agility, robustness, efficiency, and safety. Therefore, we need to improve these features by building more intelligent control software and effective hardware mechanisms. However, this is a challenging problem which involves optimization of control parameters, software architectures, and mechanical designs, where all the decisions jointly affect the motor capability of the robot. My research tackles these challenges by inventing novel optimization algorithms combined with prior knowledge and developing mathematical models for predicting the final performance of new robots. For instance, my on-going work includes learning methods for agile motions, interactive robot design software, and sim-to-real algorithms. In the long term, I aim to develop robotic companions in our home, search-and-rescue robots in disaster recovery scenes, and custom medical surgery robots that are tailored to individual patients.
Bio: Sehoon Ha is currently a research scientist at Google Brain. Before joining Google, he worked at Carnegie Mellon University as a postdoctoral researcher and Disney Research as an associate research scientist. He received his Ph.D. degree in Computer Science from Georgia Institute of Technology. His research interests lie at the intersection between computer graphics and robotics, including physics-based animation, deep reinforcement learning, and computational robot design. His work has been published at top-tier venues including ACM Transactions on Graphics, IEEE Transactions on Robotics, and International Journal of Robotics Research, nominated as the best conference paper (Top 3) in Robotics: Science and Systems, and featured in the popular media press such as IEEE Spectrum, MIT Technology Review, PBS News Hours, and Wired.