  Solving long sequential tasks poses a significant challenge in embodied
artificial intelligence. Enabling a robotic system to perform diverse
sequential tasks with a broad range of manipulation skills is an active area of
research. In this work, we present a Hybrid Hierarchical Learning framework,
the Robotic Manipulation Network (ROMAN), to address the challenge of solving
multiple complex tasks over long time horizons in robotic manipulation. ROMAN
achieves task versatility and robust failure recovery by integrating
behavioural cloning, imitation learning, and reinforcement learning. It
consists of a central manipulation network that coordinates an ensemble of
various neural networks, each specialising in distinct re-combinable sub-tasks
to generate their correct in-sequence actions for solving complex long-horizon
manipulation tasks. Experimental results show that by orchestrating and
activating these specialised manipulation experts, ROMAN generates correct
sequential activations for accomplishing long sequences of sophisticated
manipulation tasks and achieving adaptive behaviours beyond demonstrations,
while exhibiting robustness to various sensory noises. These results
demonstrate the significance and versatility of ROMAN's dynamic adaptability
featuring autonomous failure recovery capabilities, and highlight its potential
for various autonomous manipulation tasks that demand adaptive motor skills.
