Motion planning — the problem of computing physical actions to complete a specified task — has inspired some of the most theoretically rigorous and beautiful results in robotics research. But as robots proliferate in real-world applications like household service, driverless cars, warehouse automation, minimally-invasive surgery, search-and-rescue, and unmanned aerial vehicles, the classical theory appears to have fallen behind the pace of practice. At odds with the “clean” assumptions of theory, the reality is that robots must handle large amounts of noisy sensor data, uncertainty, underspecified models, nonlinear and hysteretic dynamic effects, exotic objective functions and constraints, and real-time demands. This talk will describe efforts to bring theory up to speed, in the context of three projects: 1) ladder climbing in the DARPA Robotics Challenge; 2) intelligent user interfaces for human-operated robots; and 3) navigation amongst many moving obstacles. I will present new planning algorithms and architectures whose performance is backed both by theoretical guarantees and empirical evaluation.