As we ask our robotic systems to become more capable, with the ultimate aim of deploying robots into complex and ever-changing scenarios, the vast space of potential tasks drives the need for flexibility and generalization. For all the promise of big-data machine learning, what will happen when robots deploy to our homes and workplaces and inevitably encounter new objects, new tasks, and new environments? A core challenge in generalizable robotics lies in the making and breaking of contact, where non-smooth dynamics clashes with typical assumptions in gradient-based optimization and learning. With the goal of rapid adaptation to novel settings, I’ll discuss our progress on real-time multi-contact MPC for dexterous manipulation, where we can realize dynamic motions which dynamically and intelligently make and break contact, with only a simple goal as the given objective. Control, however, requires a model, and so I will present our recent results on contact-inspired implicit model learning learning, where, by embedding convex optimization, we reshape the loss landscape and enable more accurate training, better generalization, and ultimately data efficiency. Lastly, given time, I’ll discuss how model learning and control can synergize in interesting ways: via online adaptation or by synthesizing task-relevant models which identify key aspects of multi-contact dynamics necessary to achieve a goal.