This talk present a data-driven approach that leverages real-world motion as a target for imitation, enabling robots to learn natural, adaptive, and diverse behaviors. Moving beyond simple mimicry, our work introduces a novel control framework that learns the underlying patterns within this data. This allows the system to capture multiple distinct behaviors and generate user-steerable and adaptive actions while preserving the stylistic coherence of the source motion. We address three key challenges inherent to this data-driven approach, presenting solutions that leverage a novel integration of model-based control and reinforcement learning. We will detail the core methodologies and present experimental results demonstrating the framework's effectiveness as a scalable and efficient method for learning complex, natural skills. Finally, we will discuss the potential of this approach as a general control framework for the emerging field of humanoid robotics, aiming to achieve the agile, context-aware locomotion necessary for general-purpose robots.

Dongho Kang is a Research Scientist at the Robotics and AI Institute (RAI). His research lies at the intersection of optimal control, reinforcement learning, and data-driven character animation. He received his Ph.D. in Computer Science and his M.S. in Mechanical Engineering (2016) from ETH Zurich. Previously, he obtained his B.S. from Seoul National University with a double major in Mechanical & Aerospace Engineering and Computer Science & Engineering.