AAAI Publications, Fifteenth AAAI/SIGART Doctoral Consortium

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Interactive Task-Plan Learning
Shuonan Dong

Last modified: 2010-07-05

Abstract


Low-level direct commanding of space robots can be time consuming or impractical for complex systems with many degrees of freedom. My research will adaptively raise the level of interaction between the operator and the robot by (1) allowing the robot to learn implicit plans by detecting patterns in the interaction history, and (2) enabling the human to demonstrate continuous motions through teleoperation. Learned tasks and plans are recorded for future use. I introduce a novel representation of continuous actions called parameterized probabilistic flow tubes that I hypothesize will more closely encode a human's intended motions and provide flexibility during execution in new situations. I also introduce the use of planning for plan recognition in the domain of hybrid tasks.

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