로보틱스 및 피지컬 AI

Our research develops general robot intelligence by integrating learning with priors from physics, geometry, and control. Topics below are organized by theme—each with an overview, demo videos, and selected publications. Grasping and placing rigid objects is fundamental, yet robust and general performance—across arbitrary objects, poses, and dynamically changing conditions—remains unsolved, especially for dexterous hands. Real-time feedback control using vision, touch, and proprioception is essential; manipulation should not be reduced to tracking a preplanned trajectory. Imitation learning alone is unlikely to generalize sufficiently. A promising direction is to combine physics-based feedback control, such as MPC, with reinforcement learning. Movement primitives encode reusable “muscle memory” for real-time motion generation, particularly when online optimization is too slow or the task is difficult to model. Examples include complex collision avoidance, athletic motions near kinodynamic limits, and manipulation of non-rigid materials such as water. A promising direction is to combine imitation learning, reinforcement learning, and model-based control. Explicit representations, such as object poses, shapes, and geometric relationships, enable intuitive and structured policy design. However, they may limit expressiveness and require difficult or fragile state-estimation pipelines. Implicit representations, such as learned neural features, can be more flexible and broadly applicable, but they often make policy design, interpretation, and generalization more challenging. Finding the right balance between explicit structure and learned representations for a given class of manipulation tasks remains a key challenge in robot perception. Policies developed in simulation inevi