A Humanoid Social Robot Based Approach for Indoor Environment Quality Monitoring and Well-Being Improvement

Ribino, Patrizia; Bonomolo, Marina; Lodato, Carmelo; Vitale, G.

doi:10.1007/s12369-020-00638-9

Cited by 33 publications

(18 citation statements)

References 27 publications

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“…Ambient assisted living (AAL) environments are becoming a more and more realistic opportunity due to the advances in Information and Communication technology that allow equipping environments with smart devices providing functionalities not available since recently. These smart environments may provide ubiquitous support to people, allowing to automatically control smart devices to evaluate the health state of users, to avoid dangerous situations, and to improve environmental comfort (Woźniak and Połap 2019;Lauraitis et al 2019;Ribino et al 2020;Wozniak et al 2020). Among the several applications of AAL, those in the healthcare domain are very attractive since they provide support to the elderly in their everyday life, partially relieving caregivers and/or nurses tasks.…”

Section: Introductionmentioning

confidence: 99%

Customisable assistive plans as dynamic composition of services with normed-QoS

Napoli

Ribino

Serino

2021

J Ambient Intell Human Comput

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Ambient assisted living solutions (AAL) aim to improve the quality of life, especially of elderly with cognitive and physical disabilities, by providing assistive tasks such as medication management, reminders of daily activities or medical tests. However, the loss of physical functions (such as mobility, eyesight and hearing), as well as the loss of cognitive functions (starting from amnesia to severe disabilities as dementia or Alzheimer), require appropriate solutions in order to offer personalised assistive tasks according to user needs and characteristics. In this paper, we propose a novel methodology that is based on the integration of a service-oriented approach with normative reasoning to automatically generate assistive tasks customised for different target user's profiles, and deployable in any AAL environment. The formalisation of the conceptual steps of the methodology is provided, together with a framework implementing them. The advantages of dynamic customisation obtained by decoupling the functional and not functional aspects of assistive technology are shown through a validation scenario.

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Section: Introductionmentioning

confidence: 99%

Customisable assistive plans as dynamic composition of services with normed-QoS

Napoli

Ribino

Serino

2021

J Ambient Intell Human Comput

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“…RL has experienced growth in attention and interest due to promising results in intelligent environments [10][11][12] and the areas like: playing AlphaGo [13], controlling systems in robotics [14][15][16], medical [17], atari [18] and competitive video . A method of investigating challenges posed by reporting procedures, reproducibility and proper experimental techniques through Deep Reinforcement Learning (DRL) is discussed in [19].…”

Section: Related Workmentioning

confidence: 99%

Inverse Reinforcement Learning Through Max-Margin Algorithm

Shah

Coronato

2021

Intelligent Environments 2021

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Reinforcement Learning (RL) methods provide a solution for decision-making problems under uncertainty. An agent finds a suitable policy through a reward function by interacting with a dynamic environment. However, for complex and large problems it is very difficult to specify and tune the reward function. Inverse Reinforcement Learning (IRL) may mitigate this problem by learning the reward function through expert demonstrations. This work exploits an IRL method named Max-Margin Algorithm (MMA) to learn the reward function for a robotic navigation problem. The learned reward function reveals the demonstrated policy (expert policy) better than all other policies. Results show that this method has better convergence and learned reward functions through the adopted method represents expert behavior more efficiently.

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“…ML is an application of AI that focuses on learning and improving itself from experience and without being explicitly programmed. ML emphasizes on developing algorithms that can access data and use it for self-learning [1,2,3] in an intelligent environments [4,5,6]. We are dealing with a certain number of sensors, which enable the IE [7] to be aware of the user's current action and goal.…”

Section: Introductionmentioning

confidence: 99%

An Overview of Inverse Reinforcement Learning Techniques

Shah

Pietro

2021

Intelligent Environments 2021

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In decision-making problems reward function plays an important role in finding the best policy. Reinforcement Learning (RL) provides a solution for decision-making problems under uncertainty in an Intelligent Environment (IE). However, it is difficult to specify the reward function for RL agents in large and complex problems. To counter these problems an extension of RL problem named Inverse Reinforcement Learning (IRL) is introduced, where reward function is learned from expert demonstrations. IRL is appealing for its potential use to build autonomous agents, capable of modeling others, deprived of compromising in performance of the task. This approach of learning by demonstrations relies on the framework of Markov Decision Process (MDP). This article elaborates original IRL algorithms along with their close variants to mitigate challenges. The purpose of this paper is to highlight an overview and theoretical background of IRL in the field of Machine Learning (ML) and Artificial Intelligence (AI). We presented a brief comparison between different variants of IRL in this article.

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A Humanoid Social Robot Based Approach for Indoor Environment Quality Monitoring and Well-Being Improvement

Cited by 33 publications

References 27 publications

Customisable assistive plans as dynamic composition of services with normed-QoS

Customisable assistive plans as dynamic composition of services with normed-QoS

Inverse Reinforcement Learning Through Max-Margin Algorithm

An Overview of Inverse Reinforcement Learning Techniques

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