Abstract: Autonomous robotic
operations require robots to be able to automatically generate plans.
Physically challenging environments require robots to be able to negotiate
around dynamically moving objects, cope with significant uncertainties in the
outcome of action execution, sensor limitations, and the presence of
intelligent adversaries. This seminar will cover the following four topics. First,
I will describe a planning architecture that integrates task planning, behavior
selection, and trajectory planning in a seamless manner to successfully handle
physically challenging environments. This approach provides the right balance
between deliberative and reactive planning during the execution of complex
tasks in a dynamic uncertain environment. Second, I will describe our work in
the area of physically accurate computationally efficient simulations to enable
physics-aware planning. Third, I will describe computational synthesis
techniques for automatically generating sophisticated reactive behaviors. This synthesis approach automatically generates an
initial version of an action selection policy and then gradually refines it by
detecting and fixing its shortcomings. Finally,
I will describe an approach for integrating game tree search concepts within our
planning framework to manage risks. The following applications will be used to
illustrate the approach: (1) guarding of a valuable asset by autonomous unmanned sea surface vehicles, (2) supply
mission on a rugged terrain by unmanned
ground vehicles, and (3) assembly of micro particles in a
fluidic medium using holographic optical tweezers.
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