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Inspired by the unique neurophysiology of the octopus, we propose ahierarchical framework that simplifies the coordination of multiple soft armsby decomposing control into high-level decision making, low-level motoractivation, and local reflexive behaviors via sensory feedback. When evaluatedin the illustrative problem of a model octopus foraging for food, thishierarchical decomposition results in significant improvements relative toend-to-end methods. Performance is achieved through a mixed-modes approach,whereby qualitatively different tasks are addressed via complementary controlschemes. Here, model-free reinforcement learning is employed for high-leveldecision-making, while model-based energy shaping takes care of arm-level motorexecution. To render the pairing computationally tenable, a novelneural-network energy shaping (NN-ES) controller is developed, achievingaccurate motions with time-to-solutions 200 times faster than previousattempts. Our hierarchical framework is then successfully deployed inincreasingly challenging foraging scenarios, including an arena littered withobstacles in 3D space, demonstrating the viability of our approach.

Hierarchical control and learning of a foraging CyberOctopus