Hierarchical Task and Motion Planning using Logic-Geometric Programming (HLGP)

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RSS 2019 Workshop on Robust Task and Motion Planning
Abstract: In this work, we present a hierarchical approach to task and motion planning (TAMP) within an optimization-based framework. Recent work on formulating TAMP as a logic enhanced nonlinear program has shown remarkable capabilities. However, scaling this approach to domains with many discrete decisions or longer horizons implies a computational bottleneck. To overcome this, we introduce hierarchies within this framework, where on coarser levels a problem with less discrete decisions is solved. Formally, the hierarchies are defined in a way that the resulting nonlinear programs on coarser hierarchy levels are lower bounds on the finer hierarchies. We demonstrate the generality of the approach for both a bi-manual manipulation task and a mobile manipulation scenario which includes a “worm” like walking robot.
Machine Learning and Robotics Lab, University of Stuttgart, Germany
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Max Planck Institute for Intelligent Systems, Stuttgart, Germany