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we act on the world, we care which glass is ours, which object we already have retrieved, and whether all the cows that left the barn in the morning have returned. Object individuation consists of determining the numerically distinct (distinct in the sense of distinct one) objects that articulate a given scene. Studies of object individuation in infancy typically concern the simplest individuation problem: es- tablishing whether one single object or two distinct objects are involved in some event. Adults bring a wide variety of information to bear on the task of object indi- viduation, including spatiotemporal information (one object cannot be in two places at the same time), property information (a red plastic entity seen on one oc- casion is unlikely to be the same individual as a yellow cloth entity seen on an- other), and kind information (a dog cannot be the same individual as a table). Under many circumstances, spatiotemporal information is primary; if we see an enti

This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: (i) how agents learn and represent compositional actions; (ii) how agents learn and represent compositional lexicons; (iii) the dynamics of social interaction and learning; and (iv) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test-scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.

This work investigates children’s early semantic representations of gradable adjectives (GAs) and proposes that infants perform a probabilistic analysis of the input to learn about abstract differences within this category. I first demonstrate that children as young as age three distinguish between relative (e.g., big, long), maximum standard absolute (e.g., full, straight), and minimum standard absolute (e.g., spotted, bumpy) GAs in the way that the standard of comparison is set and how it interacts with the discourse context. I then ask if adverbs enable infants to learn these differences. In a corpus analysis, I demonstrate that statistically significant patterns of adverbial modification are available to the language learner: restricted adverbs (e.g., completely) are more likely than non-restricted adverbs (e.g., very) to select for maximal GAs with bounded scales. Non-maximal GAs, which are more likely to be modified by adverbs in general, are more likely to be modified by a narrower range, predominantly composed of intensifiers (e.g., very). I then ask if language learners recruit this information when learning new adjectives. In a word learning task employing the preferential looking paradigm, I demonstrate that 30-month-olds use adverbial modifiers they are not necessarily producing to assign an interpretation to novel adjectives. Adjectives modified by completely are assigned an