Learning Generalizable Coupling Terms for Obstacle Avoidance via Low-Dimensional Geometric Descriptors

Èric Pairet, Paola Ardon, Michael Mistry, Yvan Petillot

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Unforeseen events are frequent in the real-world environments where robots are expected to assist, raising the need for fast replanning of the on-going policy to guarantee operational safety. Inspired by human behavioral studies of obstacle avoidance and route selection, this letter presents a hierarchical framework that generates reactive yet bounded obstacle avoidance behaviors through a multi-layered analysis. The framework leverages the strengths of learning techniques and the versatility of dynamic movement primitives to efficiently unify perception, decision, and action levels via environmental low-dimensional geometric descriptors. Experimental evaluation on synthetic environments and a real anthropomorphic manipulator proves the robustness and generalization capabilities of the proposed approach regardless of the obstacle avoidance scenario.
Original languageEnglish
Pages (from-to)3979-3986
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number4
Early online date23 Jul 2019
Publication statusPublished - Oct 2019


  • Collision avoidance
  • reactive and sensor-based planning
  • autonomous agents


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