Human autonomy is a core concept that helps explain the acceptance of and interaction with computer systems and AI technology. However, autonomy is often vaguely defined and conflated with related constructs. This paper disentangles autonomy by integrating the dualistic nature of positive and negative liberty from the perspective of political philosophy. Using an online vignette study with N=194 participants, we show that positive and negative liberty act as correlated but distinct dimensions of the autonomy foundation. While negative liberty predicts the sense of agency, positive liberty is a key dimension for people’s willingness to use technology. We argue that this dualistic stand - positive liberty as the freedom to pursue authentic goals, and negative liberty as the freedom from external constraints - offers a valuable and actionable perspective on human autonomy that can inform future system design and better answer the ambivalent question “how much autonomy is enough”?

As autonomous agents, from self-driving cars to virtual assistants, become increasingly present in everyday life, safe and effective collaboration depends on human understanding of agents’ intentions. Current intent communication approaches are often rigid, agent-specific, and narrowly scoped, limiting their adaptability across tasks, environments, and user preferences. A key gap remains: existing models of what to communicate are rarely linked to systematic choices of how and when to communicate, preventing the development of generalizable, multi-modal strategies. In this paper, we introduce a multidimensional design space for intent communication structured along three dimensions: Transparency (what is communicated), Abstraction (when), and Modality (how). We apply this design space to three distinct human-agent collaboration scenarios: (a) bystander interaction, (b) cooperative tasks, and (c) shared control, demonstrating its capacity to generate adaptable, scalable, and cross-domain communication strategies. By bridging the gap between intent content and communication implementation, our design space provides a foundation for designing safer, more intuitive, and more transferable agent-human interactions.

In this paper, a human-humanoid imitation system is proposed, with a focus on the kinematic model used for translating end effector positions to joint angles. The overall system comprises the humanoid robot Pepper and a Kinect v2 camera for capturing human 3D joint positions. The presented kinematic model is based on analytical solutions of Pepper’s inverse kinematics and also uses the forward kinematics. The aim of the paper is to provide insights into deriving the kinematics of robotic chains for the purpose of pose matching imitation, as well as accuracy evaluation of the derived forward and inverse kinematic solutions. The solutions of the inverse kinematics provide results with a mean error of approximately 0.2°for the angle solutions of the head joints, 0.7°for the arm joints, and 4° for the torso (leg) joints. The evaluated speed lies within a range of 0.002 to 0.08 ms. These results indicate that the presented kinematic model is an effective method for translating end effector positions to joint angles for our pose imitation application in real-time or close to it. Finally, we show preliminary results of the proposed imitation system and discuss future work.

Abstract The growing popularity of Agile management methods has led to their application to a number of areas inside, but also outside, the software development domain. However, despite many practitioner reports, research exploring whether and how Agile practices have been applied in domains beyond software development has been rather patchy with little empirical evidence. What is there behind the hype and how can other domains learn from it? To address this gap in the research, we present the findings of our study on the application of Agile management practices in other domains, including an outlook towards a potential expansion enabling Business Agility. To practitioners, this study presents examples of Scrum applied outside the software development domain including concrete practices applied. To researchers, it presents an empirical starting point for further exploration.