The success of social robots in achieving natural coexistence with humans depends on both their level of autonomy and their interactive abilities. Although a lot of robotic architectures have been suggested and many researchers have focused on human–robot interaction, a robotic architecture that can effectively combine interactivity and autonomy is still unavailable. This paper contributes to the research efforts toward this architecture in the following ways. First a theoretical analysis is provided that leads to the notion of co-evolution between the (...) agent and its environment and with other agents as the condition needed to combine both autonomy and interactivity. The analysis also shows that the basic competencies needed to achieve the required level of autonomy and the envisioned level of interactivity are similar but not the same. Secondly nine specific requirements are then formalized that should be achieved by the architecture. Thirdly a robotic architecture that tries to achieve those requirements by utilizing two main theoretical hypothesis and several insights from social science, developmental psychology and neuroscience is detailed. Lastly two experiments with a humanoid robot and a simulated agent are reported to show the potential of the proposed architecture. (shrink)

An integrated representation of large‐scale space, or cognitive map, colled PLAN, is presented that attempts to address a broader spectrum of issues than has been previously attempted in a single model. Rather than examining way‐finding as a process separate from the rest of cognition, one or the fundamental goals of this work is to examine how the wayfinding process is integrated into general cognition. One result of this approach is that the model is “heads‐up,” or scene‐based, because it takes advantage (...) of the properties of the human visual system and, particularly, the visual system's split into two pathways. The emphasis on the human location or “where” system is new to cognitive mapping and is port of an attempt to synthesize prototype theory, associative networks and location together in a connectionist system. Not all of PLAN is new, however. Many of its parts have analogues in one or another preexisting theory. What makes PLAN unique is integrating the various components into a coherent whole, and the capacity of this resulting system to speak to a wide range of constraints. Our approach emphasizes adaptiveness; thus, our focus on such issues as ease of use and efficiency of learning. The result is a model that has a stronger relationship both to the environment, and to the ways that humans interact with it, compared with previous models. The resulting model is examined in some detail and compared to other systems. (shrink)