  In dynamic operational environments, particularly in collaborative robotics,
the inevitability of failures necessitates robust and adaptable recovery
strategies. Traditional automated recovery strategies, while effective for
predefined scenarios, often lack the flexibility required for on-the-fly task
management and adaptation to expected failures. Addressing this gap, we propose
a novel approach that models recovery behaviors as adaptable robotic skills,
leveraging the Behavior Trees and Motion Generators~(BTMG) framework for policy
representation. This approach distinguishes itself by employing reinforcement
learning~(RL) to dynamically refine recovery behavior parameters, enabling a
tailored response to a wide array of failure scenarios with minimal human
intervention. We assess our methodology through a series of progressively
challenging scenarios within a peg-in-a-hole task, demonstrating the approach's
effectiveness in enhancing operational efficiency and task success rates in
collaborative robotics settings. We validate our approach using a dual-arm KUKA
robot.
