Alle Publikationen
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2020
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(2020): The Secret Life of Robots: Perspectives and Challenges for Robot’s Behaviours During Non-interactive Tasks. In: International Journal of Social Robotics, S. 1265-1278. DOI: 10.1007/s12369-020-00650-z
DOI: https://doi.org/10.1007/s12369-020-00650-z Abstract: Some applications of service robots within domestic and working environments are envisaged to be a significant part of our lives in the not too distant future. They are developed to autonomously accomplish different tasks either on behalf of or in collaboration with a human being. Robots can perceive and interpret data from the external environment, so they also collect personal information and habits; they can plan, navigate, and manipulate objects, eventually intruding in our personal space and disturbing us in the current activities. Indeed, such capabilities need to be socially enhanced to ensure their effective deployment and to favour a significant social impact. The modelling and evaluation of a service robot’s behaviour, while not interacting with a human, have only been marginally considered in the last few years. But these can be expected to play a key role in developing socially acceptable robotic applications that can be used widely. To explore this research direction, we present research objectives related to the effective development of socially-aware service robots that are not involved in tasks that require explicit interaction with a person. Such discussion aims at highlighting some of the future challenges that will be posed for the social robotics community in the next years.
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(2020): A Holistic Approach to Behavior Adaptation for Socially Assistive Robots. In: International Journal of Social Robotics, S. 617-637. DOI: 10.1007/s12369-019-00617-9
DOI: https://doi.org/10.1007/s12369-019-00617-9 Abstract: Socially assistive robotics aims at providing users with continuous support and personalized assistance, through appropriate social interactions. The design of robots capable of supporting people in heterogeneous tasks, raises several challenges among which the most relevant are the need to realise intelligent and continuous behaviours, robustness and flexibility of services and, furthermore, the ability to adapt to different contexts and needs. Artificial intelligence plays a key role in realizing cognitive capabilities like e.g., learning, context reasoning or planning that are highly needed in socially assistive robots. The integration of several of such capabilities is an open problem. This paper proposes a novel “cognitive approach” integrating ontology-based knowledge reasoning, automated planning and execution technologies. The core idea is to endow assistive robots with intelligent features in order to reason at different levels of abstraction, understand specific health-related needs and decide how to act in order to perform personalized assistive tasks. The paper presents such a cognitive approach pointing out the contribution of different knowledge contexts and perspectives, presents detailed functioning traces to show adaptation and personalization features, and finally discusses an experimental assessment proving the feasibility of the approach.
2019
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(2019): Understanding of Human Behavior with a Robotic Agent Through Daily Activity Analysis. In: International Journal of Social Robotics 11 (3), S. 437-462. DOI: 10.1007/s12369-019-00513-2
DOI: https://doi.org/10.1007/s12369-019-00513-2 Abstract: Personal assistive robots to be realized in the near future should have the ability to seamlessly coexist with humans in unconstrained environments, with the robot’s capability to understand and interpret the human behavior during human–robot cohabitation significantly contributing towards this end. Still, the understanding of human behavior through a robot is a challenging task as it necessitates a comprehensive representation of the high-level structure of the human’s behavior from the robot’s low-level sensory input. The paper at hand tackles this problem by demonstrating a robotic agent capable of apprehending human daily activities through a method, the Interaction Unit analysis, that enables activities’ decomposition into a sequence of units, each one associated with a behavioral factor. The modelling of human behavior is addressed with a Dynamic Bayesian Network that operates on top of the Interaction Unit, offering quantification of the behavioral factors and the formulation of the human’s behavioral model. In addition, light-weight human action and object manipulation monitoring strategies have been developed, based on RGB-D and laser sensors, tailored for onboard robot operation. As a proof of concept, we used our robot to evaluate the ability of the method to differentiate among the examined human activities, as well as to assess the capability of behavior modeling of people with Mild Cognitive Impairment. Moreover, we deployed our robot in 12 real house environments with real users, showcasing the behavior understanding ability of our method in unconstrained realistic environments. The evaluation process revealed promising performance and demonstrated that human behavior can be automatically modeled through Interaction Unit analysis, directly from robotic agents.
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