Robots are having an important growing role in human social life, which requires them to be able to behave appropriately to the context of interaction so as to create a successful long-term human-robot relationship. A major challenge in developing intelligent systems, which could enhance the interactive abilities of robots, is defining clear metrics and benchmarks for the different aspects of humanrobot interaction, like human and robot skills and performances, which could facilitate comparing between systems and avoid application-biased evaluations based on particular measures. The point of evaluating robotic systems through metrics and benchmarks, in addition to some recent frameworks and technologies that could endow robots with advanced cognitive and communicative abilities, are discussed in this technical report that covers the outcome of our recent workshop on current advances in cognitive robotics: Towards Intelligent Social Robots-Current Advances in Cognitive Robotics, in conjunction with the 15th IEEE-RAS Humanoids Conference-Seoul-South Korea-2015 (https://intelligent-robots-ws.ensta-paristech.fr/). Additionally, a summary of an interactive discussion session between the workshop participants and the invited speakers about different issues related to cognitive robotics research is reported.
Abstract. The work at hand addresses the question: What kind of navigation behavior do humans expect from a robot in a path crossing scenario? To this end, we developed the "Inverse Oz of Wizard" study design where participants steered a robot in a scenario in which an instructed person is crossing the robot's path. We investigated two aspects of robot behavior: (1) what are the expected actions? and (2) can we determine the expected action by considering the spatial relationship? The overall navigation strategy, that was performed the most, was driving straight towards the goal and either stop when the person and the robot came close or drive on towards the goal and pass the path of the person. Furthermore, we found that the spatial relationship is significantly correlated with the performed action and we can precisely predict the expected action by using a Support Vector Machine.
This article presents results from a multidisciplinary research project on the integration and transfer of language knowledge into robots as an empirical paradigm for the study of language development in both humans and humanoid robots. Within the framework of human linguistic and cognitive development, we focus on how three central types of learning interact and co-develop: individual learning about one's own embodiment and the environment, social learning (learning from others), and learning of linguistic capability. Our primary concern is how these capabilities can scaffold each other's development in a continuous feedback cycle as their interactions yield increasingly sophisticated competencies in the agent's capacity to interact with others and manipulate its world. Experimental results are summarized in relation to milestones in human linguistic and cognitive development and show that the mutual scaffolding of social learning, individual learning, and linguistic capabilities creates the context, conditions, and requisites for learning in each domain. Challenges and insights identified as a result of this research program are discussed with regard to possible and actual contributions to cognitive science and language ontogeny. In conclusion, directions for future work are suggested that continue to develop this approach toward an integrated framework for understanding these mutually scaffolding processes as a basis for language development in humans and robots.
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