1,173 research outputs found
Applications of Affective Computing in Human-Robot Interaction: state-of-art and challenges for manufacturing
The introduction of collaborative robots aims to make production more flexible, promoting a greater interaction between humans and robots also from physical point of view. However, working closely with a robot may lead to the creation of stressful situations for the operator, which can negatively affect task performance.
In Human-Robot Interaction (HRI), robots are expected to be socially intelligent, i.e., capable of understanding and reacting accordingly to human social and affective clues. This ability can be exploited implementing affective computing, which concerns the development of systems able to recognize, interpret, process, and simulate human affects. Social intelligence is essential for robots to establish a natural interaction with people in several contexts, including the manufacturing sector with the emergence of Industry 5.0.
In order to take full advantage of the human-robot collaboration, the robotic system should be able to perceive the psycho-emotional and mental state of the operator through different sensing modalities (e.g., facial expressions, body language, voice, or physiological signals) and to adapt its behaviour accordingly. The development of socially intelligent collaborative robots in the manufacturing sector can lead to a symbiotic human-robot collaboration, arising several research challenges that still need to be addressed.
The goals of this paper are the following: (i) providing an overview of affective computing implementation in HRI; (ii) analyzing the state-of-art on this topic in different application contexts (e.g., healthcare, service applications, and manufacturing); (iii) highlighting research challenges for the manufacturing sector
State of AI-based monitoring in smart manufacturing and introduction to focused section
Over the past few decades, intelligentization, supported by artificial intelligence (AI) technologies, has become an important trend for industrial manufacturing, accelerating the development of smart manufacturing. In modern industries, standard AI has been endowed with additional attributes, yielding the so-called industrial artificial intelligence (IAI) that has become the technical core of smart manufacturing. AI-powered manufacturing brings remarkable improvements in many aspects of closed-loop production chains from manufacturing processes to end product logistics. In particular, IAI incorporating domain knowledge has benefited the area of production monitoring considerably. Advanced AI methods such as deep neural networks, adversarial training, and transfer learning have been widely used to support both diagnostics and predictive maintenance of the entire production process. It is generally believed that IAI is the critical technologies needed to drive the future evolution of industrial manufacturing. This article offers a comprehensive overview of AI-powered manufacturing and its applications in monitoring. More specifically, it summarizes the key technologies of IAI and discusses their typical application scenarios with respect to three major aspects of production monitoring: fault diagnosis, remaining useful life prediction, and quality inspection. In addition, the existing problems and future research directions of IAI are also discussed. This article further introduces the papers in this focused section on AI-based monitoring in smart manufacturing by weaving them into the overview, highlighting how they contribute to and extend the body of literature in this area
Spatial representation for planning and executing robot behaviors in complex environments
Robots are already improving our well-being and productivity in
different applications such as industry, health-care and indoor
service applications. However, we are still far from developing (and
releasing) a fully functional robotic agent that can autonomously
survive in tasks that require human-level
cognitive capabilities. Robotic systems on the market, in fact, are
designed to address specific applications, and can only run
pre-defined behaviors to robustly repeat few tasks (e.g., assembling
objects parts, vacuum cleaning). They internal representation of the
world is usually constrained to the task they are performing, and
does not allows for generalization to other
scenarios. Unfortunately, such a paradigm only apply to a very
limited set of domains, where the environment can be assumed to be
static, and its dynamics can be handled before
deployment. Additionally, robots configured in this way will
eventually fail if their "handcrafted'' representation of the
environment does not match the external world.
Hence, to enable more sophisticated cognitive skills, we investigate
how to design robots to properly represent the environment and
behave accordingly. To this end, we formalize a representation of
the environment that enhances the robot spatial knowledge to
explicitly include a representation of its own actions. Spatial
knowledge constitutes the core of the robot understanding of the
environment, however it is not sufficient to represent what the
robot is capable to do in it. To overcome such a limitation, we
formalize SK4R, a spatial knowledge representation for robots which
enhances spatial knowledge with a novel and "functional"
point of view that explicitly models robot actions. To this end, we
exploit the concept of affordances, introduced to express
opportunities (actions) that objects offer to an agent. To encode
affordances within SK4R, we define the "affordance
semantics" of actions that is used to annotate an environment, and
to represent to which extent robot actions support goal-oriented
behaviors.
We demonstrate the benefits of a functional representation of the
environment in multiple robotic scenarios that traverse and
contribute different research topics relating to: robot knowledge
representations, social robotics, multi-robot systems and robot
learning and planning. We show how a domain-specific representation,
that explicitly encodes affordance semantics, provides the robot
with a more concrete understanding of the environment and of the
effects that its actions have on it. The goal of our work is to
design an agent that will no longer execute an action, because of
mere pre-defined routine, rather, it will execute an actions because
it "knows'' that the resulting state leads one step closer to
success in its task
Pushing AI to Wireless Network Edge: An Overview on Integrated Sensing, Communication, and Computation towards 6G
Pushing artificial intelligence (AI) from central cloud to network edge has
reached board consensus in both industry and academia for materializing the
vision of artificial intelligence of things (AIoT) in the sixth-generation (6G)
era. This gives rise to an emerging research area known as edge intelligence,
which concerns the distillation of human-like intelligence from the huge amount
of data scattered at wireless network edge. In general, realizing edge
intelligence corresponds to the process of sensing, communication, and
computation, which are coupled ingredients for data generation, exchanging, and
processing, respectively. However, conventional wireless networks design the
sensing, communication, and computation separately in a task-agnostic manner,
which encounters difficulties in accommodating the stringent demands of
ultra-low latency, ultra-high reliability, and high capacity in emerging AI
applications such as auto-driving. This thus prompts a new design paradigm of
seamless integrated sensing, communication, and computation (ISCC) in a
task-oriented manner, which comprehensively accounts for the use of the data in
the downstream AI applications. In view of its growing interest, this article
provides a timely overview of ISCC for edge intelligence by introducing its
basic concept, design challenges, and enabling techniques, surveying the
state-of-the-art development, and shedding light on the road ahead
AI augmented Edge and Fog computing: trends and challenges
In recent years, the landscape of computing paradigms has witnessed a gradual yet remarkable shift from monolithic computing to distributed and decentralized paradigms such as Internet of Things (IoT), Edge, Fog, Cloud, and Serverless. The frontiers of these computing technologies have been boosted by shift from manually encoded algorithms to Artificial Intelligence (AI)-driven autonomous systems for optimum and reliable management of distributed computing resources. Prior work focuses on improving existing systems using AI across a wide range of domains, such as efficient resource provisioning, application deployment, task placement, and service management. This survey reviews the evolution of data-driven AI-augmented technologies and their impact on computing systems. We demystify new techniques and draw key insights in Edge, Fog and Cloud resource management-related uses of AI methods and also look at how AI can innovate traditional applications for enhanced Quality of Service (QoS) in the presence of a continuum of resources. We present the latest trends and impact areas such as optimizing AI models that are deployed on or for computing systems. We layout a roadmap for future research directions in areas such as resource management for QoS optimization and service reliability. Finally, we discuss blue-sky ideas and envision this work as an anchor point for future research on AI-driven computing systems
- …