The ability to move in complex environments is indispensable both for the survival of animals and for creating fully autonomous robots. There has been an increasing interest in understanding how animals and humans can adapt their motion to cope with terrains of various structures and different complexity. For this Research Topic, we invite viewpoints regarding the role of feedback loops for the generation of robust gaits.
Both physical interactions and cognitive processes work on two levels. The physical response occurs both within the body and with the surrounding, whereas cognition may take the form of either top-down control of self-regulating local feedback loops. At the two extremes of this scale, locomotion is either generated entirely by top-down control or emerges in a fully self-organized manner. Self-organization may manifest itself on the level of an individual robot but also in swarms of interacting agents. The scope of this research topic is to contribute to our understanding of self-organizing principles for locomotion.
For top-down control, signals driving the locomotion may be generated by the nervous system of the animal or, in the case of robots, by algorithms either implementing classical robot-control strategies or simulating artificial neural networks. High-level sensory input, which encodes visual, auditory, and other information, is used to deliver feedback from the environment for decision making and movement planning. The control of actuators is then usually stiff and it is not using possible advantages of embodiment. On the other hand, for self-organized robots, the behavior emerges as a result of the interaction between individual agents or on the level of a single robot between the brain, body, and environment. Simple local laws together with the rules of interaction between different components may lead to structured behavior on the level of the system. It is not quite clear under what conditions this level of embodiment is preferable over the more stiff controlling schemes, and what its advantages and limits are. The goal of this Research Topic is to investigate different control strategies, both purely top-down or self-organized and hybrid strategies used in robotic and animal locomotion to get a better understanding of their roles.
The role of self-organization and sensory adaptation with different control strategies may be investigated both by theoretical calculations and numerical simulations, as well as via experimental approaches. Investigated systems may include rolling wheeled or other spherical- or cylindrical-shaped robots, legged robots simulating human or animal gaits, hopping robots with one or multiple legs, swimming and slithering robots. Furthermore, research works targeting neural mechanisms or biomechanics which may be implemented for understanding the role of self-organization in robotic systems are also of interest. Besides Original Research and Brief Research Report articles, we also welcome Mini-Reviews or viewpoints, providing a wide perspective together with a synthesis of existing literature. Furthermore, perspective articles may present current advances and possible future directions in the research of robotic and animal locomotion. Examples for topics of interest include but are not limited to:
- walking, rolling, swimming, flying robots
- soft robots
- locomotion gaits
- swarms of robots
- motion patterns
- synchronization
- control and learning algorithms
The ability to move in complex environments is indispensable both for the survival of animals and for creating fully autonomous robots. There has been an increasing interest in understanding how animals and humans can adapt their motion to cope with terrains of various structures and different complexity. For this Research Topic, we invite viewpoints regarding the role of feedback loops for the generation of robust gaits.
Both physical interactions and cognitive processes work on two levels. The physical response occurs both within the body and with the surrounding, whereas cognition may take the form of either top-down control of self-regulating local feedback loops. At the two extremes of this scale, locomotion is either generated entirely by top-down control or emerges in a fully self-organized manner. Self-organization may manifest itself on the level of an individual robot but also in swarms of interacting agents. The scope of this research topic is to contribute to our understanding of self-organizing principles for locomotion.
For top-down control, signals driving the locomotion may be generated by the nervous system of the animal or, in the case of robots, by algorithms either implementing classical robot-control strategies or simulating artificial neural networks. High-level sensory input, which encodes visual, auditory, and other information, is used to deliver feedback from the environment for decision making and movement planning. The control of actuators is then usually stiff and it is not using possible advantages of embodiment. On the other hand, for self-organized robots, the behavior emerges as a result of the interaction between individual agents or on the level of a single robot between the brain, body, and environment. Simple local laws together with the rules of interaction between different components may lead to structured behavior on the level of the system. It is not quite clear under what conditions this level of embodiment is preferable over the more stiff controlling schemes, and what its advantages and limits are. The goal of this Research Topic is to investigate different control strategies, both purely top-down or self-organized and hybrid strategies used in robotic and animal locomotion to get a better understanding of their roles.
The role of self-organization and sensory adaptation with different control strategies may be investigated both by theoretical calculations and numerical simulations, as well as via experimental approaches. Investigated systems may include rolling wheeled or other spherical- or cylindrical-shaped robots, legged robots simulating human or animal gaits, hopping robots with one or multiple legs, swimming and slithering robots. Furthermore, research works targeting neural mechanisms or biomechanics which may be implemented for understanding the role of self-organization in robotic systems are also of interest. Besides Original Research and Brief Research Report articles, we also welcome Mini-Reviews or viewpoints, providing a wide perspective together with a synthesis of existing literature. Furthermore, perspective articles may present current advances and possible future directions in the research of robotic and animal locomotion. Examples for topics of interest include but are not limited to:
- walking, rolling, swimming, flying robots
- soft robots
- locomotion gaits
- swarms of robots
- motion patterns
- synchronization
- control and learning algorithms
