Julian Schneider, Niels Straky, Simon Meyer, Balint V arga and S oren Hohmann Abstract -- In this paper, a method for a cooperative trajectory planning between a human and an automation is extended by a behavioral model of the human. This model can characterize the stubbornness of the human, which measures how strong the human adheres to his preferred trajectory. Accordingly, a static model is introduced indicating a link between the force in haptically coupled human-robot interactions and humans's stubbornness. The introduced stubbornness parameter enables an application-independent reaction of the automation for the cooperative trajectory planning. Simulation results in the context of human-machine cooperation in a care application show that the proposed behavioral model can quantitatively estimate the stubbornness of the interacting human, enabling a more targeted adaptation of the automation to the human behavior . I. INTRODUCTION With the advent of Industry 4.0, it's conceivable that Care 4.0 could be next [1]. There exists considerable unexplored potential in robotic systems within the caregiving area [2]. The support of intelligent systems could enable people in need of care longer independent living, possibly in their own homes [3].

A central goal in cognitive science is to characterize human knowledge of concepts and their properties. Many have used human-generated feature lists as norms for establishing the structural relationship between concepts in the human mind (McRae et al., 2005; Devereux et al., 2014; De Deyne et al., 2008; Buchanan et al., 2019), but this requires extensive human labor. Large language models (LLMs) have recently shown impressive capabilities when generating properties of objects (Hansen & Hebart, 2022) or answering questions(Ouyang et al., 2022; Brown et al., 2020; Hoffmann et al., 2022; Chowdhery et al., 2022; Wei et al., 2021) and thus suggest an avenue for more efficient characterization of human knowledge structures, but even state-of-the-art models can routinely fail on many common-sense questions of fact. GTP3-davinci, for instance, will deny that alligators are green, while asserting that they can be used to suck dust up from surfaces. Thus, human effort can generate high-quality norms, but with prohibitive costs, while LLMs can produce norms with little human effort, but with considerably less accuracy. This paper considers whether human and machine effort can combine to efficiently estimate high-quality semantic feature vectors.

AI mainly is a tool to enhance our physical or cognitive capacities. But what if we find real partners in machines? As a matter of fact, machines and humans are a perfect match because they are complementary, and we are here to decide which computer traits we need to develop and use. The idea of symbiosis between humans and machines is also settled in mass conscience creating new hopes and new phobias. Will we have a war with machines and end up as their slaves -- a slowly-thinking race unable to predict the future and make decisions properly?

University of Bristol researchrs developed a handheld robot that predicts a user's plans, then frustrates the user by rebelling against those plans. Researchers at the University of Bristol in the U.K. have developed a handheld robot that predicts a user's plans, and then frustrates the user by rebelling against those plans, demonstrating an understanding of human intention. The robots hold knowledge about the task at hand, and can help the user through guidance, fine-tuned motion, and decisions about task sequences. While the technology helps fulfill tasks quicker and with higher accuracy, users can get irritated when the robot's decisions are not in line with their own plans. The team used a prototype that can track the user's eye gaze, along with machine learning, to derive short-term predictions about intended actions.