Fusing autonomy and sociality via embodied emergence and development of behaviour and cognition from fetal period

Abstract

Human-centred AI/Robotics are quickly becoming important. Their core claim is that AI systems or robots must be designed and work for the benefits of humans with no harm or uneasiness. It essentially requires the realization of autonomy, sociality and their fusion at all levels of system organization, even beyond programming or pre-training. The biologically inspired core principle of such a system is described as the emergence and development of embodied behaviour and cognition. The importance of embodiment, emergence and continuous autonomous development is explained in the context of developmental robotics and dynamical systems view of human development. We present a hypothetical early developmental scenario that fills in the very beginning part of the comprehensive scenarios proposed in developmental robotics. Then our model and experiments on emergent embodied behaviour are presented. They consist of chaotic maps embedded in sensory–motor loops and coupled via embodiment. Behaviours that are consistent with embodiment and adaptive to environmental structure emerge within a few seconds without any external reward or learning. Next, our model and experiments on human fetal development are presented. A precise musculo-skeletal fetal body model is placed in a uterus model. Driven by spinal nonlinear oscillator circuits coupled together via embodiment, somatosensory signals are evoked and learned by a model of the cerebral cortex with 2.6 million neurons and 5.3 billion synapses. The model acquired cortical representations of self–body and multi-modal sensory integration. This work is important because it models very early autonomous development in realistic detailed human embodiment. Finally, discussions toward human-like cognition are presented including other important factors such as motivation, emotion, internal organs and genetic factors. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

From social brains to social robots: applying neurocognitive insights to human–robot interaction

“…As the contributions show, these biology-and psychology-inspired social robotics systems can lead to an impressive range of socially engaging behaviours, such as attention-grabbing emotional facial expressions [14], and even trust and theory-of-mind [13]. Crucially, these perspectives directly provide new insights for developmental psychology, for example into developmental mechanisms of social behaviour as well as clinical insights into autism spectrum condition [15,16]. Together, these contributions move away from scripted, fully top-down controlled robotic systems (as often seen in demonstrations of robotic systems), and toward fully autonomous social agents that are inspired by and build upon core concepts from human psychology and neuroscience.…”

“…In the first contribution of this section, Kuniyoshi [15] provides an integrative review of biology-inspired models of early development that are foundational for social interaction. In this framework, embodied cognition, the interaction between the agent and the environment, is crucial for the emergence of autonomous social behaviour and continuous development of these behaviours.…”

What Makes a Robot Social? A Review of Social Robots from Science Fiction to a Home or Hospital Near You

“…[34][35][36][37]), theory of mind (e.g. [38][39][40][41]), mind perception (e.g. [42][43][44][45]), intention attribution (e.g.…”

Brain-inspired model for early vocal learning and correspondence matching using free-energy optimization

“…We are also thinking of using a vocoder with an audio speaker in place of the MFCCs in order to generate a real sound with a microphone to retrieve the sound information from another channel. That is, we think that having a robot that can speak and listen will help it learn by itself and from its social environment in a more ecological fashion through embodiment following a developmental process [ 5 – 7 , 83 ]. In this line, we also envision extending our framework to visual information for audio-speech recognition [ 16 , 17 , 84 ].…”