Abstract: 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, we are beginning to
see the classical theory fall behind. The clean assumptions of theory
are at odds with the dirty reality: 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
present recent efforts to bring motion planners to bear on real robots,
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.
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