  Unforeseen events are frequent in the real-world environments where robots
are expected to assist, raising the need for fast replanning of the policy in
execution to guarantee the system and environment safety. Inspired by human
behavioural studies of obstacle avoidance and route selection, this paper
presents a hierarchical framework which generates reactive yet bounded obstacle
avoidance behaviours 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
low-dimensional geometric descriptors of the environment. Experimental
evaluation on synthetic environments and a real anthropomorphic manipulator
proves that the robustness and generalisation capabilities of the proposed
approach regardless of the obstacle avoidance scenario makes it suitable for
robotic systems in real-world environments.
