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Learning to plan with uncertain topological maps

Edward Beeching 1, 2 Jilles Dibangoye 2, 1 Olivier Simonin 2, 1 Christian Wolf 3, 1
2 CHROMA - Robots coopératifs et adaptés à la présence humaine en environnements dynamiques
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services
3 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : We train an agent to navigate in 3D environments using a hierarchical strategy including a high-level graph based planner and a local policy. Our main contribution is a data driven learning based approach for planning under uncertainty in topological maps, requiring an estimate of shortest paths in valued graphs with a probabilistic structure. Whereas classical symbolic algorithms achieve optimal results on noiseless topologies, or optimal results in a probabilistic sense on graphs with probabilistic structure, we aim to show that machine learning can overcome missing information in the graph by taking into account rich high-dimensional node features, for instance visual information available at each location of the map. Compared to purely learned neural white box algorithms, we structure our neural model with an inductive bias for dynamic programming based shortest path algorithms, and we show that a particular parameterization of our neural model corresponds to the Bellman-Ford algorithm. By performing an empirical analysis of our method in simulated photo-realistic 3D environments, we demonstrate that the inclusion of visual features in the learned neural planner outperforms classical symbolic solutions for graph based planning.
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Contributor : Edward Beeching <>
Submitted on : Wednesday, September 9, 2020 - 10:47:50 AM
Last modification on : Wednesday, October 14, 2020 - 4:00:05 AM


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  • HAL Id : hal-02933641, version 1


Edward Beeching, Jilles Dibangoye, Olivier Simonin, Christian Wolf. Learning to plan with uncertain topological maps. ECCV 2020 - 16th European Conference on Computer Vision, Aug 2020, Glasgow, United Kingdom. pp.1-24. ⟨hal-02933641⟩



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