As the aging population increases, so does the demand for personalized care and rehabilitation for individuals with neurological disorders. Effective recovery programs require intensive, task-oriented training, yet delivering continuous and individualized care remains a major challenge. Technological innovations such as wearable sensors and socially interactive robots can enhance patient monitoring and improve medical teams’ situational awareness. This paper presents a preliminary evaluation of a robot-based architecture deployed in a real clinical setting, designed to support rehabilitation tasks and patient monitoring. The results demonstrate the system’s feasibility in providing therapists with timely and accurate data, facilitating natural interactions with patients, and minimizing the need for technical interventions during use.

Social robots are increasingly being explored as interactive coaches capable of delivering personalized physical and cognitive training sessions. Improving their effectiveness entails personalized interventions through adaptive robotic systems with continuous workload quantification. This study presents a workload estimation methodology based on physiological and kinematic monitoring, designed for integration into a social robotic coach. Physical, mental, and dual-task activities were administered to 15 healthy participants, and Support Vector Regression was used to model their perceived workload levels. Physical workload was estimated with a mean absolute error (MAE) of 0.12 ± 0.01 and a correlation of 0.75 ± 0.02, demonstrating high reliability across conditions. Mental workload estimation, however, showed greater variability (MAE: 0.18 ± 0.01, correlation: 0.62 ± 0.03), particularly in cognitively demanding and high-intensity tasks. This is likely due to overlapping physiological responses to cognitive and physical demands, which introduce ambiguity in signal interpretation. The continuous workload estimation provided by the model can be leveraged to define thresholds offering a discrete interpretation of workload levels.

Combining symbolic and geometric reasoning in multiagent systems is a challenging task that involves planning, scheduling, and synchronization problems. Existing works overlooked the variability of task duration and geometric feasibility intrinsic to these systems because of the interaction between agents and the environment. We propose a combined task and motion planning approach to optimize the sequencing, assignment, and execution of tasks under temporal and spatial variability. The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task. At the task level, timeline-based planning deals with temporal constraints, duration variability, and synergic assignment of tasks. At the action level, online motion planning plans for the actual movements dealing with environmental changes. We demonstrate the approach's effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic in the shortest time possible. Compared with existing works, our approach applies to a broader range of applications and reduces the execution time of the process.

Robots acting in real-world environments may interact with humans at different levels of abstraction (e.g., process, task, physical), entailing different control and coordination challenges. When acting in social situations, robots should be able to pursue (joint) goals by behaving according to the context as well as the skills/features of involved humans. Although reliable and effective, standard control techniques may limit the adaptability of robots. Novel control technologies based on Artificial Intelligence can endow robots with the cognitive capabilities needed to achieve a higher level of autonomy in terms of flexibility, reliability, and awareness. In this context, this paper introduces a goal-oriented acting framework based on timeline-based planning and execution. The framework is evaluated on a realistic Human-Robot Collaboration manufacturing scenario. Results show the capability of dealing with the uncontrollable dynamics of humans achieving effective and reliable collaborations.

Abstract Socially assistive robotics (SAR) aims at designing robots capable of guaranteeing social interaction to human users in a variety of assistance scenarios that range, e.g., from giving reminders for medications to monitoring of Activity of Daily Living, from giving advices to promote an healthy lifestyle to psychological monitoring. Among possible users, frail older adults deserve a special focus as they present a rich variability in terms of both alternative possible assistive scenarios (e.g., hospital or domestic environments) and caring needs that could change over time according to their health conditions. In this perspective, robot behaviors should be customized according to properly designed user models. One of the long-term research goals for SAR is the realization of robots capable of, on the one hand, personalizing assistance according to different health-related conditions/states of users and, on the other, adapting behaviors according to heterogeneous contexts as well as changing/evolving needs of users. This work proposes a solution based on a user model grounded on the international classification of functioning, disability and health (ICF) and a novel control architecture inspired by the dual-process theory. The proposed approach is general and can be deployed in many different scenarios. In this paper, we focus on a social robot in charge of the synthesis of personalized training sessions for the cognitive stimulation of older adults, customizing the adaptive verbal behavior according to the characteristics of the users and to their dynamic reactions when interacting. Evaluations with a restricted number of users show good usability of the system, a general positive attitude of users and the ability of the system to capture users personality so as to adapt the content accordingly during the verbal interaction.

Cyber-Physical Systems constitute one of the core concepts in Industry 4.0 aiming at realizing production systems that combine the efforts of human workers, robots, and intelligent entities. This is particularly crucial in Human-Robot Collaboration manufacturing where a tight peer-to-peer interaction between humans and intelligent autonomous robots is necessary. The work proposes the integration of novel Artificial Intelligence technologies to enhance the flexibility and adaptability of collaborative robots. The integrated functionalities allow a collaborative robot to autonomously recognize the tasks a human worker performs, and accordingly adapt its behavior. The approach is deployed on a real HRC scenario showing the functioning of the developed cognitive capabilities and the increased flexibility of resulting collaborations.

Cyber-Physical Systems constitute one of the core concepts in Industry 4.0 aiming at realizing production systems that combine the efforts from human workers, robots and intelligent entities. This is particularly true in Human-Robot Collaboration manufacturing where a tight peer-to-peer interaction between humans and (intelligent) autonomous robots is necessary. Such production systems need a holistic integration along different levels of abstraction and coordination for deploying effective and safe control solutions. We propose the use of novel Artificial Intelligence technologies to enhance flexibility and adaptability of these collaborative systems. Our aim is to advance the classical human-aware paradigm that considers the worker as an anonymous acting entity, in favour of a user-aware paradigm, that considers a worker as profiled user characterized with a number of specific features influencing the “shape” of the collaboration.

For an effective deployment in manufacturing, Collaborative Robots should be capable of adapting their behavior to the state of the environment and to keep the user safe and engaged during the interaction. Artificial Intelligence (AI) enables robots to autonomously operate understanding the environment, planning their tasks and acting to achieve some given goals. However, the effective deployment of AI technologies in real industrial environments is not straightforward. There is a need for engineering tools facilitating communication and interaction between AI engineers and Domain experts. This paper proposes a novel software tool, called TENANT (Tool fostEriNg Ai plaNning in roboTics) whose aim is to facilitate the use of AI planning technologies by providing domain experts like e.g., production engineers, with a graphical software framework to synthesize AI planning models abstracting from syntactic features of the underlying planning formalism.

A key aspect of robotic assistants is their ability to contextualize their behavior according to different needs of assistive scenarios. This work presents an ontology-based knowledge representation and reasoning approach supporting the synthesis of personalized behavior of robotic assistants. It introduces an ontological model of health state and functioning of persons based on the International Classification of Functioning, Disability and Health. Moreover, it borrows the concepts of affordance and function from the literature of robotics and manufacturing and adapts them to robotic (physical and cognitive) assistance domain. Knowledge reasoning mechanisms are developed on top of the resulting ontological model to reason about stimulation capabilities of a robot and health state of a person in order to identify action opportunities and achieve personalized assistance. Experimental tests assess the performance of the proposed approach and its capability of dealing with different profiles and stimuli.

Socially assistive robots should provide users with personalized assistance within a wide range of scenarios such as hospitals, home or social settings and private houses. Different people may have different needs both at the cognitive/physical support level and in relation to the preferences of interaction. Consequently the typology of tasks and the way the assistance is delivered can change according to the person with whom the robot is interacting. The authors' long-term research goal is the realization of an advanced cognitive system able to support multiple assistive scenarios with adaptations over time. We here show how the integration of model-based and model-free AI technologies can contextualize robot assistive behaviors and dynamically decide what to do (assistive plan) and how to do it (assistive plan execution), according to the different features and needs of assisted persons. Although the approach is general, the paper specifically focuses on the synthesis of personalized therapies for (cognitive) stimulation of users.

Combining task and motion planning efficiently in human-robot collaboration (HRC) entails several challenges because of the uncertainty conveyed by the human behavior. Tasks plan execution should be continuously monitored and updated based on the actual behavior of the human and the robot to maintain productivity and safety. We propose control-based approach based on two layers, i.e., task planning and action planning. Each layer reasons at a different level of abstraction: task planning considers high-level operations without taking into account their motion properties; action planning optimizes the execution of high-level operations based on current human state and geometric reasoning. The result is a hierarchical framework where the bottom layer gives feedback to top layer about the feasibility of each task, and the top layer uses this feedback to (re)optimize the process plan. The method is applied to an industrial case study in which a robot and a human worker cooperate to assemble a mosaic.

This paper presents a novel comprehensive framework called ROS-TiPlEx (Timeline-based Planning and Execution with ROS) to provide a shared environment in which experts in robotics and planning can easily interact to, respectively, encode information about low-level robot control and define task planning and execution models. ROS-TiPlEx aims at facilitating the interaction between both kind of experts, thus, enhancing and possibly speeding up the process of an integrated control design. ROS-TiPlEx is the first tool addressing the connection of ROS and timeline-based planning.

The deployment of assistive robots in everyday life scenarios and their capability of providing an effective and useful support for independent living is an open and challenging research problem. The development of suitable robot control systems requires effective solutions for addressing issues concerning performance, reliability, flexibility and proactivity. In this work, we propose an AI-based cognitive architecture aiming at integrating knowledge representation with automated planning and execution techniques in order to endow assistive robots with proactivity and self-configuration capabilities.

The collaboration between humans and robots is a current technological trend that faces various challenges, among these the seamless integration of the respective working capabilities. Industrial robots have demonstrated their capacity to meet the needs of many applications, offering accuracy and efficiency, while humans have both experience and the capability to elaborate over such experience that are absolutely not replaceable at any time. Clearly a symbiotic integration of humans and robots in working scenarios opens to new problems: for an effective collaboration an intelligent coordination is required. This paper presents an interactive environment for facilitating the collaboration between humans and a robot in performing shared tasks in industrial environments. In particular we introduce a tool based on AI planning technology to help the smooth intertwining of activities of the two actors in the work environment. The paper presents a case study from a real world environment, describes a comprehensive architectural approach to the problem of coordinated interaction, and then presents details on the current status of the tool.

This paper presents the mid-term outcome of the Generic Evolutionary Control Knowledge-based mOdule (Gecko) research project, i.e., a layered architecture to implement a cooperative model-based control agent for a Reconfigurable Transportation System (RTSs). A manufacturing plant is here conceived as multiple independent modules to implement alternative inbound logistic systems' configurations. To support this capability of the mechatronic hardware, an integrated solution is proposed using a knowledge-based approach to support a timeline-based planning and control module responsible for managing both the node regular activities and reconfiguration activities. A cooperation layer dedicated to multi-module coordination completes the overall architecture.

Planning for real world problems with explicit temporal constraints is a challenging problem. Among several approaches, the use of flexible timelines in Planning and Scheduling (P&S) has demonstrated to be successful in a number of concrete applications, such as, for instance, autonomous space systems. A flexible timeline describes an envelope of possible solutions which can be exploited by an executive system for robust on-line execution. A remarkable research effort has been dedicated to design, build and deploy software environments, like EUROPA, ASPEN, and APSI-TRF, for the synthesis of timeline-based P&S applications. Several attempts have also been made to characterize the concept of timelines. Nevertheless, a formal characterization of flexible timelines and plans is still missing. This paper presents a formal account of flexible timelines aiming at providing a general semantics for related planning concepts such as domains, goals, problems, constraints and flexible plans. Some basic properties of the defined concepts are also stated and proved. A simple running example inspired by a real world planning domain is exploited to illustrate the proposed formal notions. Finally, a planning tool, called Extensible Planning and Scheduling Library (EPSL), is briefly presented, which is able to generate flexible plans that are compliant with the given semantics.