Publikation
Hierarchical Policy Blending as Inference for Reactive Robot Control
Kay Hansel; Julen Urain; Jan Peters; Georgia Chalvatzaki
In: IEEE International Conference on Robotics and Automation, ICRA 2023, London, UK, May 29 - June 2, 2023. IEEE International Conference on Robotics and Automation (ICRA), Pages 10181-10188, IEEE, 2023.
Zusammenfassung
Motion generation in cluttered, dense, and dy-
namic environments is a central topic in robotics, rendered as a
multi-objective decision-making problem. Current approaches
trade-off between safety and performance. On the one hand,
reactive policies guarantee a fast response to environmental
changes at the risk of suboptimal behavior. On the other hand,
planning-based motion generation provides feasible trajectories,
but the high computational cost may limit the control frequency
and, thus, safety. To combine the benefits of reactive policies
and planning, we propose a hierarchical motion generation
method. Moreover, we employ probabilistic inference methods
to formalize the hierarchical model and stochastic optimization.
We realize this approach as a weighted product of stochastic,
reactive expert policies, where planning is used to adaptively
compute the optimal weights over the task horizon. This
stochastic optimization avoids local optima and proposes feasi-
ble reactive plans that find paths in cluttered and dense environ-
ments. Our extensive experimental study in planar navigation
and 7DoF manipulation shows that our proposed hierarchical
motion generation method outperforms both myopic reactive
controllers and online re-planning methods. Additional material
available at https://sites.google.com/view/hipbi