Mastering the sensorimotor capabilities of our body is a skill that we acquire and refine over time, starting at the prenatal stages of development. This learning process is linked to brain development and is shaped by the rich set of multimodal information experienced while exploring and interacting with the environment.
Evidence coming from neuroscience suggests that the brain forms and maintains body representations as the main strategy to this mastering. Although it is still not clear how this knowledge is represented in our brain, it is reasonable to think that such internal models of the body undergo a continuous process of adaptation. They need to match growing corporal dimensions during development, as well as temporary changes in the characteristics of the body, such as the transient morphological alterations produced by the usage of tools.
In the robotics community there is an increasing interest in reproducing similar mechanisms in artificial agents, mainly motivated by the aim of producing autonomous adaptive systems that can deal with complexity and uncertainty in human environments. Although promising results have been achieved in the context of sensorimotor learning and autonomous generation of body representations, it is still not clear how such low-level representations can be scaled up to more complex motor skills and how they can enable the development of cognitive capabilities.
Recent evidence from behavioural and brain studies suggests that processes of mental simulations of action-perception loops are likely to be executed in our brain and are dependent on internal motor representations. The capability to simulate sensorimotor experience might represent a key mechanism behind the implementation of further cognitive skills, such as self-detection, self-other distinction and imitation. However, empirical investigation on the functioning of similar processes in the brain and on their implementation in artificial agents is fragmented.
In this Research Topic, we aim to condensate the latest developments and ideas on how to implement these skills in robotics. In being provided with a rich set of actuators and sensors, humanoid robots provide a perfect test bed for these investigations. Their characteristics are more easily comparable with ours, thus allowing for better human-robot interaction in human-designed environments.
We welcome manuscripts that address new paradigms for learning and integrating multimodal sensorimotor information in artificial agents, the reuse of the sensorimotor experience for cognitive development and the further construction of more complex strategies and behaviours using these concepts. We also welcome interdisciplinary studies from developmental, cognitive and brain sciences that target similar topics. Studies are encouraged to provide comprehensive examination of the topics proposed, with the aim of providing a more coherent understanding of the key mechanisms behind plasticity in internal body representations and mental simulation processes for cognitive development.
Mastering the sensorimotor capabilities of our body is a skill that we acquire and refine over time, starting at the prenatal stages of development. This learning process is linked to brain development and is shaped by the rich set of multimodal information experienced while exploring and interacting with the environment.
Evidence coming from neuroscience suggests that the brain forms and maintains body representations as the main strategy to this mastering. Although it is still not clear how this knowledge is represented in our brain, it is reasonable to think that such internal models of the body undergo a continuous process of adaptation. They need to match growing corporal dimensions during development, as well as temporary changes in the characteristics of the body, such as the transient morphological alterations produced by the usage of tools.
In the robotics community there is an increasing interest in reproducing similar mechanisms in artificial agents, mainly motivated by the aim of producing autonomous adaptive systems that can deal with complexity and uncertainty in human environments. Although promising results have been achieved in the context of sensorimotor learning and autonomous generation of body representations, it is still not clear how such low-level representations can be scaled up to more complex motor skills and how they can enable the development of cognitive capabilities.
Recent evidence from behavioural and brain studies suggests that processes of mental simulations of action-perception loops are likely to be executed in our brain and are dependent on internal motor representations. The capability to simulate sensorimotor experience might represent a key mechanism behind the implementation of further cognitive skills, such as self-detection, self-other distinction and imitation. However, empirical investigation on the functioning of similar processes in the brain and on their implementation in artificial agents is fragmented.
In this Research Topic, we aim to condensate the latest developments and ideas on how to implement these skills in robotics. In being provided with a rich set of actuators and sensors, humanoid robots provide a perfect test bed for these investigations. Their characteristics are more easily comparable with ours, thus allowing for better human-robot interaction in human-designed environments.
We welcome manuscripts that address new paradigms for learning and integrating multimodal sensorimotor information in artificial agents, the reuse of the sensorimotor experience for cognitive development and the further construction of more complex strategies and behaviours using these concepts. We also welcome interdisciplinary studies from developmental, cognitive and brain sciences that target similar topics. Studies are encouraged to provide comprehensive examination of the topics proposed, with the aim of providing a more coherent understanding of the key mechanisms behind plasticity in internal body representations and mental simulation processes for cognitive development.
