2023/02/11 by Chia-Hsien Shih, Shih, Chia-Hsien, Noel Naughton +13 · 2 citations
Agricultural and Biological Sciences · Neuroscience · Psychology · #Action Observation and Synchronization #Cephalopods and Marine Biology #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #FOS: Electrical engineering #Robotics (cs.RO) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2302.05811
openalex publication_date 2023/02/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Inspired by the unique neurophysiology of the octopus, we propose a hierarchical framework that simplifies the coordination of multiple soft arms by decomposing control into high-level decision making, low-level motor activation, and local reflexive behaviors via sensory feedback. When evaluated in the illustrative problem of a model octopus foraging for food, this hierarchical decomposition results in significant improvements relative to end-to-end methods. Performance is achieved through a mixed-modes approach, whereby qualitatively different tasks are addressed via complementary control schemes. Here, model-free reinforcement learning is employed for high-level decision-making, while model-based energy shaping takes care of arm-level motor execution. To render the pairing computationally tenable, a novel neural-network energy shaping (NN-ES) controller is developed, achieving accurate motions with time-to-solutions 200 times faster than previous attempts. Our hierarchical framework is then successfully deployed in increasingly challenging foraging scenarios, including an arena littered with obstacles in 3D space, demonstrating the viability of our approach.