Fast and Robust Motion Planning with Noisy Data using Machine Learning
نویسندگان
چکیده
We give an overview of a new set of learningbased algorithms for fast and robust motion planning with noisy sensor data. These include improved search in configuration spaces using instance-based learning, and robust proximity queries in configuration spaces with noisy data. We demonstrate the performance of these new proximity and planning algorithms on the PR2 robot with noisy point-cloud data.
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