<html><head><meta http-equiv="Content-Type" content="text/html charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class=""><div class=""><br class=""></div><div class=""><div class=""><br class=""></div><div class="">Topic 1: "Emergence of the Cognitive Mechanisms for Cooperation“</div><div class=""><br class=""></div><div class="">The problem of evolution of cooperation and the emergence of collective behavior, which cuts across diverse disciplines like Economics, Physics, Biology, Psychology, and Political, Cognitive and Computer Sciences, is still one of the greatest interdisciplinary challenges that science faces today. Mathematical and simulation techniques from Evolutionary Game Theory have been proven useful to study this problem. To understand the evolutionary mechanisms that promote and maintain cooperative behavior in various societies, it is important to take into account the intrinsic complexity of individuals partaking therein, namely their cognitive and complex decision-making processes. The outcome of many social and economic interactions is defined by not only predictions that individuals make about the behavior and intentions of other individuals, but also the cognitive mechanism that the others adopt to make their decision. Modelling research has shown that the way the decision process is modeled, at different levels of complexity, using different cognitive architectures such as high-order theory of mind, cognitive hierarchy theory, cognitive control or bounded rationality, has varying influence on the equilibrium that can be reached in dynamical<br class="">systems.<br class=""><br class="">Abundant evidence exists that shows that humans (and many other species) are capable of complex cognitive skills, viz. theory-of-mind, intention recognition, hypothetical, counterfactual and reactive reasoning, emotion guidance, learning, preferences, commitment and morality. To better understand how these mechanisms make cooperation possible they need to be modeled within the context of evolutionary processes. In other words, we should aim to understand how systems that were successfully developed in Artificial Intelligence and Cognitive Sciences to explain human behavior handle themselves in the light of Darwin’s evolutionary theory.<br class=""><br class="">The purpose of this interdisciplinary Research Topic is to bring together researchers working on various aspects of evolution of cooperation, evolutionary psychology, artificial intelligence and cognitive modelling, thus providing an integrated forum to unite these different research perspectives.<br class=""><br class="">This Research Topic will deepen and clarify the role of cognitive and reasoning skills for the evolution of cooperative behavior and their integration in for instance evolutionary game theory. The results and observations will have important implications for the design of self-organized and distributed multi-agent systems (e.g. multi-robot systems), showing how cognition might influence agent cooperation and coordination, and the extent to which cognition may advantageously be implemented into social agents.</div><div class=""><br class=""></div><div class=""><a href="http://journal.frontiersin.org/researchtopic/5790/emergence-of-the-cognitive-mechanisms-for-cooperation" class="">http://journal.frontiersin.org/researchtopic/5790/emergence-of-the-cognitive-mechanisms-for-cooperation</a></div><div class=""></div><div class=""><br class=""></div><div class=""><br class=""></div><div class=""><br class=""></div><div class="">Topic 2: "Mapping Human Sensory-Motor Skills for Manipulation onto the Design and Control of Robots“</div><div class=""><br class=""></div><div class="">Humans are endowed with extraordinary sensory-motor capabilities that enable a successful interaction with and exploration of the environment, as is the case of human manipulation. Understanding and modeling these capabilities represents an important topic not only for neuroscience but also for robotics in a mutual inspiration, both to inform the design and control of artificial systems and, at the same time, to increase knowledge on the biological side. Within this context, synergies -- i.e., goal directed actions that constrain multi DOFs of human body and can be defined at kinematic, muscular, neural level -- have gained an increasing attention as a general simplified approach to shape the development of simple and effective artificial devices.<br class=""><br class="">The execution of such purposeful sensory-motor primitives on the biological side leverages on the interplay of the sensory-motor control at central and peripheral level, and the interaction of human body with the external world. This interaction is particularly important considering the new concept of robotic soft manipulation, i.e. soft, adaptable yet robust robotic hands that can deform with the external environment to multiply their grasping and manipulation capabilities. Under this regard, a preeminent role is deserved to touch, being the skin our primary organ to shape our knowledge of the external world and, hence, to modify it, in interaction with the efferent parts.<br class=""><br class="">Thus, with this research proposal we would like to investigate the mutual inspiration between neuroscience and robotics, and how it is possible to translate neuroscientific findings on human manipulation into engineering guidelines for simplified systems able to take full advantage from the interaction and hence exploitation of environmental constraints for task accomplishment and knowledge acquisition.<br class=""><br class="">This Research Topic is intrinsically interdisciplinary and aims to integrate expertise from different domains of research. Topics of interest include, but are not limited to:<br class="">-<span class="Apple-tab-span" style="white-space:pre"> </span>Neuroscientific investigation and technical tools for studying human behavior in interaction with the environment;<br class="">-<span class="Apple-tab-span" style="white-space:pre"> </span>Modeling of the sense of touch and proprioception, including tactile illusions as means to give insights into sensory information gathering and processing;<br class="">-<span class="Apple-tab-span" style="white-space:pre"> </span>Methods for transferring and mapping human behavior to robots;<br class="">-<span class="Apple-tab-span" style="white-space:pre"> </span>Design, sensing and control of simple and effective soft robotic hands and sensory systems for robots, taking inspiration from the human example.</div><div class=""><br class=""></div><div class=""><a href="http://journal.frontiersin.org/researchtopic/5808/mapping-human-sensory-motor-skills-for-manipulation-onto-the-design-and-control-of-robots" class="">http://journal.frontiersin.org/researchtopic/5808/mapping-human-sensory-motor-skills-for-manipulation-onto-the-design-and-control-of-robots</a></div><div class=""></div><div class=""><br class=""></div><div class=""><br class=""></div><div class=""><br class=""></div><div class=""><br class=""></div><div class="">Topic 3: "Modeling Play in Early Infant Development“</div><div class=""><br class=""></div><div class="">This Frontiers Research Topic focuses on the question: Can we develop computers or robots that play and develop like children? Approaches to this question involves the elaboration and study of computational models of infant play with the perspective of two complementary disciplines. Firstly, developmental psychology benefits from such models to formulate theories and conjectures of infant play which can be tested and evaluated through experimental studies. Secondly, the new field of developmental robotics looks toward infant development for inspiration, data, and guidance, in order to build models of learning that may be useful both for better understanding of human development and for engineering autonomous learning in robots and other systems. <br class=""><br class="">These fields have common ground in this very active and significant research area, investigating how babies learn and grow cognitively, and testing our knowledge in the concrete world of computer models. A major characteristic of early human development is the open-ended acquisition of new abilities and competencies. Human infants are born helpless yet they actively become familiar with their environment and their own body through spontaneous exploration and interaction with others. Within a few months of rapid learning and development, they have acquired quite sophisticated sensory-motor and social competences. New skills appear to sprout from current competences as experience builds along a continuous trajectory of action and interaction. In particular, such open-ended learning is readily seen in the ubiquitous behaviour known as play.<br class=""><br class="">Play can be used to describe an expansive range of exploratory activities, but the concept currently lacks a sufficiently unifying theoretical framework. Here we focus on forms of play which involve free and spontaneous intrinsically motivated exploration of actions, objects, places or tasks and activities in varying contexts, outside motivation to fulfill basic physiological needs like feeding and without external goals set by social peers. Such forms of exploration may involve the search for novelty or surprise, can be goal-free but also involve self-generated goals which are pursued for their intrinsic “interestingness”. For example, when encountering novel objects or events, infants will often display pleasure in the interaction, try to repeat the experience and show enjoyment of their own activity. This suggests an enactive approach which Jerome Bruner called “learning by doing”. Von Hofsten describes play as “the purposeful seeking of enjoyable action possibilities”, and Vicky Bruce stresses the immersive aspects in terms of several features of “free-flow” play. <br class=""><br class="">From developmental robotics, work on these ideas have explored both solitary play with objects and early interactive play with others as a generative behaviour that combines fragments of past experience with new sensory-motor events in differing contexts. <br class="">Computational models of play have been proposed, for example based on forms of novelty or information gain as an intrinsic driver, leading to designs for investigations on “curious robots”. <br class=""><br class="">The aim of this Frontiers Research Topic is to present international state-of-the-art research from naturalistic or experimental infant studies and computational/robot modelling, on early infant play behaviour. The focus will be on the very earliest forms of play, because this is concurrent with increasing perception and understanding of the “physics of the world”, e.g. perceptions of objects, causality, and interactions. Many interesting questions arise: for example, how does play emerge and what is its relation to goal-free motor babbling? How does play relate to object understanding and world knowledge. How does intrinsically motived self-generation of goals relate to future extrinsically motivated goal generation and goal attribution? How far can the world be explored through the paradigm of play? How can we best understand more about infant cognition from modelling these concepts on robots? We solicit leading contributions eliciting experience and original research on computational modelling of psychological experiments about these topics, as well as experimental and theoretical papers that increase understanding of these important issues and core concepts in infants and machines.</div><div class=""><br class=""></div><div class=""><a href="http://journal.frontiersin.org/researchtopic/5527/modeling-play-in-early-infant-development" class="">http://journal.frontiersin.org/researchtopic/5527/modeling-play-in-early-infant-development</a></div><div class=""><br class=""></div><div class=""><br class=""></div><div class=""><br class=""></div><div class=""></div><div class=""><br class=""></div><div class="">Topic 4: "Machine Learning Methods for High-Level Cognitive Capabilities in Robotics“</div><div class=""><br class=""></div><div class="">Integrating multi-level sensory-motor and cognitive capabilities is essential for developing robotic systems that can adaptively act in our daily environment in active collaboration with humans. In this research topic, we aim to share knowledge about the state-of-the-art machine learning methods that contribute to modeling sensory-motor and cognitive capabilities in robotics with a special emphasis on adaptive high-level cognition.<br class="">Our daily environment is full of uncertainties with complex objects and challenging tasks. A robot is not only required to deal with things appropriately in a physical manner but also required to perform linguistic and/or logical tasks in the real world. When a robot attempts to communicate and collaborate with human users in a real-world environment, e.g. the RoboCup@Home environment, bridging high-level and low-level cognitive capabilities appropriately is crucial. The high-level cognitive capabilities include logical inference, planning, and language. In contrast, the low-level cognitive capability includes physical control, behavioral motion generation and sensory perception.<br class=""><br class="">Conventionally, symbol-based and/or rule-based approaches have been employed to model high-level cognitive capabilities in robotics. However, it has been pointed out that such conventional methods could not deal with the uncertainty that is inevitably found in the physical environment and natural human-robot communication.<br class=""><br class="">Recent advances in machine learning methods, including deep learning and hierarchical Bayesian modeling, are providing us with new possibilities to integrate high-level and low-level cognitive capabilities in robotics. It became clear that such learning methods are indispensable to create robots that can effectively deal with uncertainty in the real world.<br class=""><br class="">This research topic includes multimodal communication, emergence of communication, learning motor skills, segmentation of time-series information, concept formation, probabilistic programing and reasoning in robotics, language acquisition, human-robot communication and collaboration based on machine learning, deep learning for robotics, and Bayesian modeling for high-level cognitive capabilities. Note that the list is non-exhaustive. This research topic is closely related to the Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics 2016 held at the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016). We welcome not only the papers presented at the workshop, but also new papers related to the research topic.<br class=""><br class="">According to this, the Research topic welcomes various articles that contribute to the progress in machine learning methods for high-level cognitive capabilities in robotics.</div><div class=""><br class=""></div><div class=""><a href="http://journal.frontiersin.org/researchtopic/5334/machine-learning-methods-for-high-level-cognitive-capabilities-in-robotics" class="">http://journal.frontiersin.org/researchtopic/5334/machine-learning-methods-for-high-level-cognitive-capabilities-in-robotics</a></div><div class=""><br class=""></div><div class=""><br class=""></div><div class=""></div><div class=""><br class=""></div><div class=""><br class=""></div><div class="">----------------------------------------------------------</div><div class=""><br class=""></div><div class="">Priv.-Doz. Dr. Florian Roehrbein<br class="">Program Director HBP Neurorobotics<br class=""><a href="http://neurorobotics.net/" class="">http://neurorobotics.net/</a><br class=""><br class="">Technical University of Munich</div><div class="">Department of Informatics VI<br class="">Boltzmannstr. 3, 85748 Garching, Germany</div></div><div class=""><br class=""></div><div class=""><br class=""></div></body></html>