Generalizable Policy Learning in the Physical World

While the study of generalization has played an essential role in many application domains of machine learning (e.g., image recognition and natural language processing), it did not receive the same amount of attention in common frameworks of policy learning (e.g., reinforcement learning and imitation learning) at the early stage for reasons such as policy optimization is difficult and benchmark datasets are not quite ready yet. Generalization is particularly important when learning policies to interact with the physical world. The spectrum of such policies is broad: the policies can be high-level, such as action plans that concern temporal dependencies and causalities of environment states; or low-level, such as object manipulation skills to transform objects that are rigid, articulated, soft, or even fluid.In the physical world, an embodied agent can face a number of changing factors such as \textbf{physical parameters, action spaces, tasks, visual appearances of the scenes, geometry and topology of the objects}, etc. And many important real-world tasks involving generalizable policy learning, e.g., visual navigation, object manipulation, and autonomous driving. Therefore, learning generalizable policies is crucial to developing intelligent embodied agents in the real world. Though important, the field is very much under-explored in a systematic way.Learning generalizable policies in the physical world requires deep synergistic efforts across fields of vision, learning, and robotics, and poses many interesting research problems. This workshop is designed to foster progress in generalizable policy learning, in particular, with a focus on the tasks in the physical world, such as visual navigation, object manipulation, and autonomous driving. We envision that the workshop will bring together interdisciplinary researchers from machine learning, computer vision, and robotics to discuss the current and future research on this topic.