ArticleFrontiers in neurorobotics2017
Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System.
Article in Frontiers in neurorobotics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Wax Protrusions on Anti-Adhesive Plant Surfaces and Their Interactions with Insect Adhesive Pads: A Mechanical Interpretation.Biomimetics (Basel, Switzerland) · 2024Review
- Reinforcement learning as a robotics-inspired framework for insect navigation: from spatial representations to neural implementation.Frontiers in computational neuroscience · 2024Article
- Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review.Biomimetics (Basel, Switzerland) · 2023Review
- Ataxia-associated DNA repair genes protect theFrontiers in neural circuits · 2023Article
- Insect-Inspired Robots: Bridging Biological and Artificial Systems.Sensors (Basel, Switzerland) · 2021Review
- Achieving natural behavior in a robot using neurally inspired hierarchical perceptual control.iScience · 2021Article
- Minding the gap: learning and visual scanning behaviour in nocturnal bull ants.The Journal of experimental biology · 2021Article
- NeuroVis: Real-Time Neural Information Measurement and Visualization of Embodied Neural Systems.Frontiers in neural circuits · 2021Article
- A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks.Frontiers in neurorobotics · 2018Review
- Integrating Brain and Biomechanical Models-A New Paradigm for Understanding Neuro-muscular Control.Frontiers in neuroscience · 2018Article
- Adaptive Control Strategies for Interlimb Coordination in Legged Robots: A Review.Frontiers in neurorobotics · 2017Review
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Authors and funding
4 authors.
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Abstract
In nature, insects show impressive adaptation and learning capabilities. The proposed computational model takes inspiration from specific structures of the insect brain: after proposing key hypotheses on the direct involvement of the mushroom bodies (MBs) and on their neural organization, we developed a new architecture for motor learning to be applied in insect-like walking robots. The proposed model is a nonlinear control system based on spiking neurons. MBs are modeled as a nonlinear recurrent spiking neural network (SNN) with novel characteristics, able to memorize time evolutions of key parameters of the neural motor controller, so that existing motor primitives can be improved. The adopted control scheme enables the structure to efficiently cope with goal-oriented behavioral motor tasks. Here, a six-legged structure, showing a steady-state exponentially stable locomotion pattern, is exposed to the need of learning new motor skills: moving through the environment, the structure is able to modulate motor commands and implements an obstacle climbing procedure. Experimental results on a simulated hexapod robot are reported; they are obtained in a dynamic simulation environment and the robot mimicks the structures of
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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.