Self-organized adaptation of a simple neural circuit enables complex robot behaviour
By S. Steingrube, Marc Timme, Florentin Wörgötter, P. Manoonpong. arXiv.org, 2010
344 citations recorded by the source corpus.
Controlling sensori-motor systems in higher animals or complex robots is a challenging combinatorial problem, because many sensory signals need to be simultaneously coordinated into a broad behavioural spectrum. To rapidly interact with the environment, this control needs to be fast and adaptive. Present robotic solutions operate with limited autonomy and are mostly restricted to few behavioural patterns. Here we introduce chaos control as a new strategy to generate complex behaviour of an autonomous robot. In the presented system, 18 sensors drive 18 motors by means of a simple neural control circuit, thereby generating 11 basic behavioural patterns (for example, orienting, taxis, self-protection and various gaits) and their combinations. The control signal quickly and reversibly adapts to new situations and also enables learning and synaptic long-term storage of behaviourally useful motor responses. Thus, such neural control provides a powerful yet simple way to self-organize versatile behaviours in autonomous agents with many degrees of freedom. Mimicking even the simplest of animal behaviour, such as walking along uneven terrain, is a challenging task. A study finds that incorporating a simple but inherently chaotic pattern generator into the control system of an autonomous robot allows it to show adaptive behaviour, enabling it to successfully navigate through a complex environment.
Subfields: Condensed Matter Physics, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/c11c867e8a2e8a1cab8cdc39ce6cb5788f3606f5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Condensed Matter Physics
In Statistical and Nonlinear Physics
- Jianxi Gao
- Boleslaw K. Szymanski
- Daqing Li
- Xueming Liu
- H. Eugene Stanley
- Manqing Ma
- Garrick Orchard
- Ladislav Kristoufek
Citations in the corpus
- Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook (2021, background): “Several groups have demonstrated their capability to generate locomotion patterns of insect-like robots [112].”
- Network resilience (2020, background): “between systems into consideration, many studies have been carried out to study the robustness in interdependent networks [14, 810, 811, 812, 813, 814, 815, 816, 648, 619, 817, 818], interconnected networks [819, 820, 649], multi-layered networks [821, 822, 823, 824, 825], multiplex networks [826], ”