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.