Research on human computation and crowdsourcing has concentrated on tasks that can be accomplished remotely over the Internet. We introduce a general class of problems we call crowdphysics (CP)---crowdsourcing tasks that require people to collaborate and synchronize both in time and physical space. As an illustrative example, we focus on a crowd-powered delivery service---a specific CP instance where people go about their daily lives, but have the opportunity to carry packages to be delivered to specific locations or individuals. Each package is handed off from person to person based on overlaps in time and space until it is delivered. We formulate CP tasks by reduction to a graph-planning problem, and analyze the performance using a large sample of geotagged tweets as a proxy for people's location. We show that packages can be delivered with remarkable speed and coverage. These results hold for the case when we know people's future locations and also when routing without global knowledge, making only local greedy decisions. To our knowledge, this is the first empirical evidence that dynamic networks of mobile individuals are highly navigable.