Artificial intelligence is revolutionizing formal education, fueled by innovations in learning assessment, content generation, and instructional delivery. Informal, lifelong learning settings have been the subject of less attention. We provide a proof-of-concept for an embodied book discussion companion, designed to stimulate conversations with readers about particularly creative metaphors in fiction literature. We collect ratings from 26 participants, each of whom discuss Jane Austen's "Pride and Prejudice" with the robot across one or more sessions, and find that participants rate their interactions highly. This suggests that companion robots could be an interesting entryway for the promotion of lifelong learning and cognitive exercise in future applications.
Educational scenarios to which social robots have been deployed have been primarily formal settings with child learners @cite_9 @cite_11 @cite_8 @cite_6 @cite_24 @cite_13 @cite_27 . Our focus is on a different setting: informal, conversational lifelong learning. is the process of acquiring knowledge and or exercising cognitive faculties across the human lifespan, outside of traditional academic contexts. Research involving social robots in lifelong learning scenarios has been scarce, with most work deploying social robots to adult populations focusing on psychological or physical healthcare needs instead @cite_19 @cite_18 @cite_10 @cite_28 . However, @cite_2 designed a human-robot music guessing game to stimulate cognition in older adults suffering from dementia, and @cite_26 created a social robot to scaffold motivation in adult second language learners. @cite_21 also explored second language learning in adults, although their robot's behaviors were originally designed with children in mind. A common theme across these systems is the absence of open-ended conversation: all cases utilize buttons and multiple-choice answers as their input. The inability to converse naturally limits a robot's potential to engage in cognitively meaningful interactions, particularly when dealing with more subjective topics like literature or metaphor interpretation.
@cite_18 @cite_26 @cite_8 @cite_28 @cite_10 @cite_9 @cite_21 @cite_6 @cite_24 @cite_19 @cite_27 @cite_2 @cite_13 @cite_11
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In contrast to conventional teaching agents (including robots) that were designed to play the role of human teachers or caregivers, we propose the opposite scenario in which robots receive instruction or care from children. We hypothesize that by using this care-receiving robot, we may construct a new educational framework whose goal is to promote children's spontaneous learning by teaching through their teaching the robot. In this paper, we describe the introduction of a care-receiving robot into a classroom at an English language school for Japanese children (3--6 years of age) and then conduct an experiment to evaluate if the care-receiving robot can promote their learning using English verbs. The results suggest that the idea of a care-receiving robot is feasible and that the robot can help children learn new English verbs efficiently. In addition, we report on investigations into several forms of teaching performed by children, which were revealed through observations of the children, parent interviews, and other useful knowledge. These can be used to improve the design of care-receiving robots for educational purposes. Future applications for personal robots motivate research into developing robots that are intelligent in their interactions with people. Toward this goal, in this paper we present an integrated socio-cognitive architecture to endow an anthropomorphic robot with the ability to infer mental states such as beliefs, intents, and desires from the observable behavior of its human partner. The design of our architecture is informed by recent findings from neuroscience and embodies cognition that reveals how living systems leverage their physical and cognitive embodiment through simulation-theoretic mechanisms to infer the mental states of others. We assess the robot's mindreading skills on a suite of benchmark tasks where the robot interacts with a human partner in a cooperative scenario and a learning scenario. In addition, we have conducted human subjects experiments using the same task scenarios to assess human performance on these tasks and to compare the robot's performance with that of people. In the process, our human subject studies also reveal some interesting insights into human behavior. In this paper, we present a framework for guiding autonomous learning in robot systems. The paradigm we introduce allows a robot to acquire new skills according to an intrinsic motivation function that finds behavioral affordances. Affordances-in the sense of (Gibson, Toward and Ecological Psychology, Hillsdale, NJ, 1977)-describe the latent possibilities for action in the environment and provide a direct means of organizing functional knowledge in embodied systems. We begin by showing how a robot can assemble closed-loop action primitives from its sensory and motor resources, and then show how these primitives can be sequenced into multi-objective policies. We then show how these policies can be assembled hierarchically to support incremental and cumulative learning. The main contribution of this paper demonstrates how the proposed intrinsic motivator for affordance discovery can cause a robot to both acquire such hierarchical policies using reinforcement learning and then to generalize these policies to new contexts. As the framework is described, its effectiveness and applicability is demonstrated through a longitudinal learning experiment on a bimanual robot. Navigating and path planning in environments with limited a priori knowledge is a fundamental challenge for mobile robots. Robots operating in human-occupied environments must also respect sociocontextual boundaries such as personal workspaces. There is a need for robots to be able to navigate in such environments without having to explore and build an intricate representation of the world. In this paper, a method for supplementing directly observed environmental information with indirect observations of occupied space is presented. The proposed approach enables the online inclusion of novel human positional traces and environment information into a probabilistic framework for path planning. Encapsulation of sociocontextual information, such as identifying areas that people tend to use to move through the environment, is inherently achieved without supervised learning or labelling. Our method bootstraps navigation with indirectly observed sensor data, and leverages the flexibility of the Gaussian process (GP) for producing a navigational map that sampling based path planers such as Probabilistic Roadmaps (PRM) can effectively utilise. Empirical results on a mobile platform demonstrate that a robot can efficiently and socially-appropriately reach a desired goal by exploiting the navigational map in our Bayesian statistical framework.