Joint summary from https://mitpress.mit.edu/books/developmental-robotics

Developmental robotics is the interdisciplinary approach to the autonomous design of behavioural and cognitive capabilities in artificial agents (robots) that takes direct inspiration from the developmental principles and mechanisms observed in the natural cognitive systems.  It relies on a highly interdisciplinary effort of empirical developmental sciences such as developmental psychology, neuroscience, and comparative psychology, and computational and engineering disciplines such as robotics and artificial intelligence.  The implementation of these principles and mechanisms into a robot-s control architecture and the testing through experiments where the robot interacts with its physical and social environment simultaneously permits the validation of such principles and the actual design of complex behavioural and mental capabilities in robots. Developmental psychology and developmental robotics mutually benefit from such a combined effort.

Synonym terms of Developmental Robotics include cognitive developmental robotics, autonomous mental development as well as epigenetic robotics \cite{Cangelosi18}. The  later term borrows the term ‘epigenetic’ from Piaget’s Epigenetic Theory of human development, where the child’s cognitive system develops as a result of the interaction between genetic predispositions and the organism’s interaction with the environment.  Therefore, ‘Epigenetic robotics’ was justified by Piaget’s stress on the importance of the role of interaction with the environment (later complemented with the emphasis on social interaction of Zlatev and Balkenius in 2001), and in particular, on the sensorimotor bases of higher-order cognitive capabilities. 
Due to the challenges of robotics domain not only on acquisition of skills in an incremental manner but also in open-ended environments, it is perhaps worth noticing that the state of the art is not as advanced as in other more constrained well defined unique task settings. Because of the importance of embodiment in robotics for open ended learning \cite{Cangelosi18} and because of the scarcity of continual learning strategies on robotics and merely assessed  on perception tasks, we believe both fields methodologies should borrow from each other. The blend could result not only in more effective or robust lifelong learning but also in less forgetful agents (i.e., the main rationale behind CL), which has not yet empirically shown is impossible to reach. We hope this survey helps reach this goal.

Dynamical systems development: In math it is characterized by complex changes over time in the phase state. The complex interaction of nonlinear phenomena results in the production of unpredictable states of the system, often referred to as emergent states. This concept was borrowed by developmental psychologists \cite{Thelen94} to explain child development as the emergent product of the intricate and dynamic interaction of many decentralized and local interactions related to the child growing body and brain and her environment.


Some definitions: 

Novelty, curiosity and surprise: focuses on the autonomy of learning, i.e. on the agents freedom to choose what, when and how it will learn. Intrinsic motivation (IM) is a mechanism to drive autonomous learning, not only in developmental robotics but also more broadly within the field of machine learning \cite{Mirolli13}\cite{Oudeyer07}. IM is task-independent, the agent could be placed in a completely new environment with no prior knowledge  or experience, and through self-directed exploration the robot will potentially learn not only important features of the environment but also the behavioural skills necessary for dealing with the environment. Second, IM promotes the hierarchical learning rather than solving a specific, predefined task. 

The Theory of MInd (ToM): A robot can use its own theory of mind to improve the interaction with human users, for example. It aims at reading the intention of the others and reacting appropriately to the emotional, attentional and cognitive states of the other agents, to anticipate their reactions, and to modify its own behaviour to satisfy these expectation and needs \cite{Scasellati02}.

Cascades: developmental theories refer to the far reach of early developments on later ones in terms of the “developmental cascade”.

Saliency in robotics: 
In robotics vision, a set of feature extraction methods typically can be applied to derive a saliency map \cite{Itti01}, i.e. the identification of the parts of the image that are important for the robot behaviour. It can be created, e.g., by combining a set of feature extraction method for color, motion, orientation and brightness. These features are combined to generate the whole saliency  map used by the model to fixate the objects.