@inbook {2007,
	title = {Anticipating Rewards in Continuous Time and Space: A Case Study in Developmental Robotics},
	booktitle = {Anticipatory Behavior in Adaptive Learning Systems: From Brains to Individual and Social Behavior},
	series = {Lecture Notes in Artificial Intelligence},
	volume = {4520},
	year = {2007},
	pages = {267{\textendash}284},
	publisher = {Springer},
	organization = {Springer},
	address = {Berlin, Heidelberg},
	abstract = {This paper presents the first basic principles, implementation and experimental results of what could be regarded as a new approach to reinforcement learning, where agents{\textemdash}physical robots interacting with objects and other agents in the real world{\textemdash}can learn to anticipate rewards using their sensory inputs. Our approach does not need discretization, notion of events, or classification, and instead of learning rewards for the different possible actions of an agent in all the situations, we propose to make agents learn only the main situations worth avoiding and reaching. However, the main focus of our work is not reinforcement learning as such, but modeling cognitive development on a small autonomous robot interacting with an {\textquotedblleft}adult{\textquotedblright} caretaker, typically a human, in the real world; the control architecture follows a Perception-Action approach incorporating a basic homeostatic principle. This interaction occurs in very close proximity, uses very coarse and limited sensory-motor capabilities, and affects the {\textquotedblleft}well-being{\textquotedblright} and affective state of the robot. The type of anticipatory behavior we are concerned with in this context relates to both sensory and reward anticipation. We have applied and tested our model on a real robot.},
	isbn = {978-3-540-74261-6},
	doi = {10.1007/978-3-540-74262-3_15},
	url = {https://www.springer.com/gp/book/9783540742616},
	author = {Arnaud J Blanchard and Lola Ca{\~n}amero},
	editor = {Martin V Butz and Olivier Sigaud and Giovanni Pezzulo and Gianluca Baldassarre}
}
@article {2006,
	title = {Attachment Bonds for Human-Like Robots},
	journal = {International Journal of Humanoid Robotics},
	volume = {3},
	year = {2006},
	pages = {301{\textendash}320},
	abstract = {If robots are to be truly integrated in humans{\textquoteright} everyday environment, they cannot be simply (pre-)designed and directly taken "off the shelf" and embedded into a real-life setting. Also, technical excellence and human-like appearance and "superficial" traits of their behavior are not enough to make social robots trusted, believable, and accepted. Fuller and deeper integration into human environments would require that, like children, robots develop embedded in the social environment in which they will fulfill their roles. An important element to bootstrap and guide this integration is the establishment of affective bonds between the "infant" robot and the adults among whom it develops, from whom it learns, and who it will later have to look after. In this paper, we present a Perception{\textendash}Action architecture and experiments to simulate imprinting {\textemdash} the establishment of strong attachment links with a "caregiver" {\textemdash} in a robot. Following recent theories, we do not consider imprinting as rigidly timed and irreversible, but as a more flexible phenomenon that allows for further adaptation as a result of reward-based learning through experience. After the initial imprinting, adaptation is achieved in the context of a history of "affective" interactions between the robot and a human, driven by "distress" and "comfort" responses in the robot.},
	issn = {0219-8436},
	doi = {10.1142/S0219843606000771},
	url = {http://www.worldscientific.com/doi/abs/10.1142/S0219843606000771},
	author = {Lola Ca{\~n}amero and Arnaud J Blanchard and Jacqueline Nadel}
}
