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

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.