We present a novel integration between a computational framework for modeling attention-driven perception and cognition (ARCADIA) with a cognitive robotic architecture (DIARC), demonstrating how this integration can be used to drive the gaze behavior of a robotic platform. Although some previous approaches to controlling gaze behavior in robots during human-robot interactions have relied either on models of human visual attention or human cognition, ARCADIA provides a novel framework with an attentional mechanism that bridges both lower-level visual and higher-level cognitive processes. We demonstrate how this approach can produce more natural and human-like robot gaze behavior. In particular, we focus on how our approach can control gaze during an interactive object learning task. We present results from a pilot crowdsourced evaluation that investigates whether the gaze behavior produced during this task increases confidence that the robot has correctly learned each object.

Regular irradiation of indoor environments with ultraviolet C (UVC) light has become a regular task for many in-door settings as a result of COVID-19, but current robotic systems attempting to automate it suffer from high costs and inefficient irradiation. In this paper, we propose a purpose-made inexpensive robotic platform with off-the-shelf components and standard navigation software that, with a novel algorithm for finding optimal irradiation locations, addresses both shortcomings to offer affordable and efficient solutions for UVC irradiation. We demonstrate in simulations the efficacy of the algorithm and show a prototypical run of the autonomous integrated robotic system in an indoor environment. In our sample instances, our proposed algorithm reduces the time needed by roughly 30% while it increases the coverage by a factor of 35% (when compared to the best possible placement of a static light).

Past work on acquisition of word-object associations in robots has focused on either fast instruction-based methods which accept highly constrained input or gradual cross-situational learning methods, but not a mixture of both. In this paper, we present an integrated robotic system which allows for a combination of these methods to contribute to the task of learning the labels of objects in AI agents. We demonstrate the expanded word learning capabilities in the outcome system and how learning from both human-human and human-robot dialogues can be achieved in one integrated system.

Learning new knowledge from single instructions and being able to apply it immediately is highly desirable for artificial agents. We provide the first demonstration of spoken instruction-based one-shot object and action learning in a cognitive robotic architecture and briefly discuss the architectural modifications required to enable such fast learning, demonstrating the new capabilities on a fully autonomous robot.

Being able to quickly and naturally teach robots new knowledge is critical for many future open-world human-robot interaction scenarios. In this paper we present a novel approach to using natural language context for one-shot learning of visual objects, where the robot is immediately able to recognize the described object. We describe the architectural components and demonstrate the proposed approach on a robotic platform in a proof-of-concept evaluation.

Introspection mechanisms are employed in agent architectures toimprove agent performance. However, there is currently no approach tointrospection that makes automatic adjustments at multiple levels inthe implemented agent system. We introduce our novel multi-levelintrospection framework that can be used to automatically adjustarchitectural configurations based on the introspection results at theagent, infrastructure and component level. We demonstrate the utilityof such adjustments in a concrete implementation on a robot where thehigh-level goal of the robot is used to automatically configure thevision system in a way that minimizes resource consumption whileimproving overall task performance.