YouTube Link:
Slides for the Talk:
Abstract for the Talk:
Achieving robotic embodied intelligence requires robots to learn to balance perception and action seamlessly, just like humans—even from an early age—navigate and manipulate their environments through a continuous cycle of integrated perception, action, and learning. In this keynote, I will explore how structure can be integrated at different layers of robot learning algorithms to enable faster and safer learning while fostering generalized behaviors.
I will demonstrate how embedding and leveraging structure within representation learning, motion generation, decision-making, and exploration strategies in robot reinforcement learning leads to more efficient, safe, and versatile behaviors in complex robotic systems, allowing for effective coordination across multiple embodiments. This structured approach lays a foundation for the future of autonomous robot learning systems to efficiently adapt to, and integrate in new environments. Just as structured learning enables humans to achieve natural intelligence, I argue that structured robot learning is essential for developing robotic embodied intelligence, ultimately guiding us toward smart and safe robotic assistance in our daily lives. ✨