The development of autonomous robotic systems offers significant potential for performing complex tasks with precision and consistency. Recent advances in Artificial Intelligence (AI) have enabled more capable intelligent automation systems, addressing increasingly complex challenges. However, this progress raises questions about human roles in such systems. Human-Centered AI (HCAI) aims to balance human control and automation, ensuring performance enhancement while maintaining creativity, mastery, and responsibility. For real-world applications, autonomous robots must balance task performance with reliability, safety, and trustworthiness. Integrating HCAI principles enhances human-robot collaboration and ensures responsible operation.This paper presents a bibliometric analysis of intelligent autonomous robotic systems, utilizing SciMAT and VOSViewer to examine data from the Scopus database. The findings highlight academic trends, emerging topics, and AI’s role in self-adaptive robotic behaviour, with an emphasis on HCAI architecture. These insights are then projected onto the IBM MAPE-K architecture, with the goal of identifying how these research results map into actual robotic autonomous systems development efforts for real-world scenarios.

Safety can be listed as one of the most important aspects of every working environment, from the low risky to the most dangerous one. Construction sites can be clearly identified among the riskiest working fields, mainly because several complex and fast maneuvers are executed in a very crowded, dynamic and uncertain scenario. Specifically referring to construction machines, typical operations such as digging, earthmoving, heavy weights loading/unloading, etc. become incredibly risky if the machine stability is not guaranteed by the operator. To this purpose, this paper proposes a practical implementation of a dynamic fall prediction system for excavators based on the concept of the Zero Moment Point (ZMP). A commercially available excavator has been dynamically modelled and equipped with a set of sensors to reconstruct the state of the machine. When the proposed system is running, an alarm rings if a critical condition is reached, thus allowing the operator to avoid risky and potentially harmful situations.

An effective robotic wrist represents a key enabling element in robotic manipulation, especially in prosthetics. In this paper, we propose an under-actuated wrist system, which is also adaptable and allows to implement different under-actuation schemes. Our approach leverages upon the idea of soft synergies — in particular the design method of adaptive synergies — as it derives from the field of robot hand design. First we introduce the design principle and its implementation and function in a configurable test bench prototype, which can be used to demonstrate the feasibility of our idea. Furthermore, we report on results from preliminary experiments with humans, aiming to identify the most probable wrist pose during the pre-grasp phase in activities of daily living. Based on these outcomes, we calibrate our wrist prototype accordingly and demonstrate its effectiveness to accomplish grasping and manipulation tasks.

Rendering forces to the user is one of the main goals of haptic technology. While most force-feedback interfaces are robotic manipulators, attached to a fixed frame and designed to exert forces on the users while being moved, more recent haptic research introduced two novel important ideas. On one side, cutaneous stimulation aims at rendering haptic stimuli at the level of the skin, with a distributed, rather than, concentrated approach. On the other side, wearable haptics focuses on highly portable and mobile devices, which can be carried and worn by the user as the haptic equivalent of an mp3 player. This paper presents a light and simple wearable device (CUFF) for the distributed mechano-tactile stimulation of the user's arm skin with pressure and stretch cues, related to normal and tangential forces, respectively. The working principle and the mechanical and control implementation of the CUFF device are presented. Then, after a basic functional validation, a first application of the device is shown, where it is used to render the grasping force of a robotic hand (the Pisa/IIT SoftHand). Preliminary results show that the device is capable to deliver in a reliable manner grasping force information, thus eliciting a good softness discrimination in users and enhancing the overall grasping experience.