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Learning Obstacle Representations for Neural Motion Planning

Robin Strudel 1 Ricardo Garcia 2 Justin Carpentier 1 Jean-Paul Laumond 1 Ivan Laptev 1 Cordelia Schmid 1
1 WILLOW - Models of visual object recognition and scene understanding
DI-ENS - Département d'informatique de l'École normale supérieure, Inria de Paris
2 Thoth - Apprentissage de modèles à partir de données massives
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann
Abstract : Motion planning and obstacle avoidance is a key challenge in robotics applications. While previous work succeeds to provide excellent solutions for known environments, sensor-based motion planning in new and dynamic environments remains difficult. In this work we address sensor-based motion planning from a learning perspective. Motivated by recent advances in visual recognition, we argue the importance of learning appropriate representations for motion planning. We propose a new obstacle representation based on the PointNet architecture [1] and train it jointly with policies for obstacle avoidance. We experimentally evaluate our approach for rigid body motion planning in challenging environments and demonstrate significant improvements of the state of the art in terms of accuracy and efficiency.
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Submitted on : Monday, September 21, 2020 - 1:57:41 PM
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  • HAL Id : hal-02944348, version 1
  • ARXIV : 2008.11174

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Robin Strudel, Ricardo Garcia, Justin Carpentier, Jean-Paul Laumond, Ivan Laptev, et al.. Learning Obstacle Representations for Neural Motion Planning. CoRL 2020 - Conference on Robot Learning, Nov 2020, Cambridge MA / Virtual, United States. ⟨hal-02944348⟩

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