Hierarchical organization of behavior and learning is widespread in animals and robots, among others to facilitate dealing with multiple tasks. In hierarchical reinforcement learning, agents usually have to learn to recombine or modulate low-level behaviors when facing a new task, which costs time that could potentially be saved by employing intrinsically adaptive low-level controllers. At the same time, although there exists extensive research on the use of pattern generators as low-level controllers for robot motion, the effect of their potential adaptivity on high-level performance on multiple tasks has not been explicitly studied. This paper investigates this effect using a dynamically simulated hexapod robot that needs to complete a high-level learning task on terrains of varying complexity. Results show that as terrain difficulty increases and adaptivity to environmental disturbances becomes more important, low-level controllers with a degree of instability have a positive impact on high-level performance. In particular, these controllers provide an initial performance boost that is maintained throughout learning, showing that their instability does not negatively affect their predictability, which is important for learning.
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