As in human children, learning is expected to be cumulative and of progressively increasing complexity, and to result from self-exploration of the world in combination with social interaction.
Alan Turing, as well as a number of other pioneers of cybernetics, already formulated those questions and the general approach in 1950,[1] but it is only since the end of the 20th century that they began to be investigated systematically.
As many of the theories coming from these sciences are verbal and/or descriptive, this implies a crucial formalization and computational modeling activity in developmental robotics.
The sensorimotor and social spaces in which humans and robot live are so large and complex that only a small part of potentially learnable skills can actually be explored and learnt within a life-time.
As developmental robotics is a relatively new research field and at the same time very ambitious, many fundamental open challenges remain to be solved.
First of all, existing techniques are far from allowing real-world high-dimensional robots to learn an open-ended repertoire of increasingly complex skills over a life-time period.
Actually, no experiments lasting more than a few days have been set up so far, which contrasts severely with the time needed by human infants to learn basic sensorimotor skills while equipped with brains and morphologies which are tremendously more powerful than existing computational mechanisms.
Another important challenge is to allow robots to perceive, interpret and leverage the diversity of multimodal social cues provided by non-engineer humans during human-robot interaction.
Actually, the very existence and need for symbols in the brain are actively questioned, and alternative concepts, still allowing for compositionality and functional hierarchies are being investigated.
Another open problem is the understanding of the relation between the key phenomena investigated by developmental robotics (e.g., hierarchical and modular sensorimotor systems, intrinsic/extrinsic/social motivations, and open-ended learning) and the underlying brain mechanisms.
The interaction of evolutionary mechanisms, unfolding morphologies and developing sensorimotor and social skills will thus be a highly stimulating topic for the future of developmental robotics.