ArticleFrontiers in robotics and AI2022
Lessons for Robotics From the Control Architecture of the Octopus.
Article in Frontiers in robotics and AI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- A detachable crawling robotic hand.Nature communications · 2026Article
- Diverse musculature layers in three species of octopus support precise motor control yet lack smooth muscle.Royal Society open science · 2025Article
- Molecular and Morphological Circuitry of the Octopus Sucker Ganglion.The Journal of comparative neurology · 2025Article
- Learning from Octopuses: Cutting-Edge Developments and Future Directions.Biomimetics (Basel, Switzerland) · 2025Review
- In vivo electrophysiology recordings and computational modeling can predict octopus arm movement.Bioelectronic medicine · 2025Article
- Neuronal segmentation in cephalopod arms.Nature communications · 2025Article
- Single unit electrophysiology recordings and computational modeling can predict octopus arm movement.bioRxiv : the preprint server for biology · 2024Article
- Neuronal segmentation in cephalopod arms.Research square · 2024Article
- Neuronal segmentation in cephalopod arms.bioRxiv : the preprint server for biology · 2024Article
- Mechanosensory Control of Locomotion in Animals and Robots: Moving Forward.Integrative and comparative biology · 2023Review
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biological and artificial agents are faced with many of the same computational and mechanical problems, thus strategies evolved in the biological realm can serve as inspiration for robotic development. The octopus in particular represents an attractive model for biologically-inspired robotic design, as has been recognized for the emerging field of soft robotics. Conventional global planning-based approaches to controlling the large number of degrees of freedom in an octopus arm would be computationally intractable. Instead, the octopus appears to exploit a distributed control architecture that enables effective and computationally efficient arm control. Here we will describe the neuroanatomical organization of the octopus peripheral nervous system and discuss how this distributed neural network is specialized for effectively mediating decisions made by the central brain and the continuous actuation of limbs possessing an extremely large number of degrees of freedom. We propose top-down and bottom-up control strategies that we hypothesize the octopus employs in the control of its soft body. We suggest that these strategies can serve as useful elements in the design and development of soft-bodied robotics.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.