• Novel end-to-end causal decision-making framework for autonomous robots. • Integration of causal discovery and reasoning within the ROS ecosystem. • PeopleFlow: a context-sensitive Gazebo simulator for human-robot interaction. • Causal routing significantly improves robot safety and battery efficiency. • Preemptive task refusal based on causal battery consumption estimates. The growing integration of robots in shared environments—such as warehouses, shopping centres, and hospitals—demands a deep understanding of the underlying dynamics and human behaviours, including how, when, and where individuals engage in various activities and interactions. This knowledge goes beyond simple correlation studies and requires a more comprehensive causal analysis. By leveraging causal inference to model cause-and-effect relationships, we can better anticipate critical environmental factors and enable autonomous robots to plan and execute tasks more effectively. To this end, we propose a novel causality-based decision-making framework that reasons over a learned causal model to assist the robot in deciding when and how to complete a given task. In the examined use case—i.e., a warehouse shared with people—we exploit the causal model to estimate battery usage and human obstructions as factors influencing the robot’s task execution. This reasoning framework supports the robot in making informed decisions about task timing and strategy. To achieve this, we developed also PeopleFlow, a new Gazebo-based simulator designed to model context-sensitive human-robot spatial interactions in shared workspaces. PeopleFlow features realistic human and robot trajectories influenced by contextual factors such as time, environment layout, and robot state, and can simulate a large number of agents. While the simulator is general-purpose, in this paper we focus on a warehouse-like environment as a case study, where we conduct an extensive evaluation benchmarking our causal approach against a non-causal baseline. Our findings demonstrate the efficacy of the proposed solutions, highlighting how causal reasoning enables autonomous robots to operate more efficiently and safely in dynamic environments shared with humans.

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.

Autonomous robots are agents that interact with the environment and perform tasks using their own abilities (i.e., skills) without continuous human intervention. However, in real-life scenarios, intelligent robots also need to discover the effects of their actions and understand how to save them for future use. This task appears time-consuming and very challenging, especially in a social environment populated by people who typically modify their behaviors based on the context and can dynamically impact the robot’s decision-making process. This paper aims to investigate the feasibility of autonomously creating an abstract representation of the domain knowledge from the data acquired during the robot’s exploration, inferring causal-effect relations between the executed actions, and learning context-aware symbols that describe the environment states at high level, ultimately producing a PDDL-based description of the domain. With this purpose, a new framework that relies on ROS, the standard de-facto in robotics, and ROSPlan has been developed to facilitate the transfer into several robotic platforms. Preliminary results suggest the possibility of describing the robot’s experience per option via context-based symbols that are consistently learned by the system from a few data samples.

Deploying robots in human-shared environments requires a deep understanding of how nearby agents and objects interact. Employing causal inference to model cause-and-effect relationships facilitates the prediction of human behaviours and enables the anticipation of robot interventions. However, a significant challenge arises due to the absence of implementation of existing causal discovery methods within the ROS ecosystem, the standard de-facto framework in robotics, hindering effective utilisation on real robots. To bridge this gap, in our previous work we proposed ROS-Causal, a ROS-based framework designed for onboard data collection and causal discovery in human-robot spatial interactions. In this work, we present an experimental evaluation of ROS-Causal both in simulation and on a new dataset of human-robot spatial interactions in a lab scenario, to assess its performance and effectiveness. Our analysis demonstrates the efficacy of this approach, showcasing how causal models can be extracted directly onboard by robots during data collection. The online causal models generated from the simulation are consistent with those from lab experiments. These findings can help researchers to enhance the performance of robotic systems in shared environments, firstly by studying the causal relations between variables in simulation without real people, and then facilitating the actual robot deployment in real human environments. ROS-Causal: https://lcastri.github.io/roscausal

This article proposes a novelshared intelligencesystem for brain-machine interface (BMI) teleoperated mobile robots where user’s intention and robot’s intelligence are concurrent elements equally participating in the decision process. We designed the system to rely onpoliciesguiding the robot’s behavior according to the current situation. We hypothesized that the fusion of thesepolicieswould lead to the identification of the next, most probable, location of the robot in accordance with the user’s expectations. We asked 13 healthy subjects to evaluate the system during teleoperated navigation tasks in a crowded office environment with a keyboard (reliable interface) and with 2-class motor imagery (MI) BMI (uncertain control channel). Experimental results show that ourshared intelligencesystem 1) allows users to efficiently teleoperate the robot in both control modalities; 2) it ensures a level of BMI navigation performances comparable to the keyboard control; 3) it actively assists BMI users in accomplishing the tasks. These results highlight the importance of investigating advanced human-machine interaction (HMI) strategies and introducing robotic intelligence to improve the performances of BMI actuated devices.

The P300 is an endogenous event-related potential (ERP) involved in several cognitive processes, apparently preserved between adults and children. In the pediatric age it shows different age-related characteristics. Its application in Brain Computer Interface (BCI) pediatric research remains to date still unclear. The aim of this paper is to give an overview of the maturational aspects of the visual P300, that could be used for developing BCI paradigms in the pediatric age.

This work proposes an overview of the recent applications of brain-computer interface (BCI) technology for pediatric populations. Current BCIs have demonstrated the possibility to provide an alternative communication and interaction channel for people suffering from severe motor disabilities. However, to date research has been predominantly conducted in adults, only a few systems have been applied to pediatric population. A survey was carried out to show the ongoing trends of using BCI systems with children. We discuss three areas of applications where BCI might be helpful to children - "Communication & Control", "BCI Gaming for Neurofeedback Training" and "Rehabilitation" - highlighting the current limitations and the possible future challenges.

The increasing interest of the research community in the intertwined fields of brain-machine interface (BMI) and robotics has led to the development of a variety of brain-actuated devices, ranging from powered wheelchairs and telepresence robots to wearable exoskeletons. Nevertheless, in most cases, the interaction between the two systems is still rudimentary, allowing only an unidirectional simple communication from the BMI to the robot that acts as a mere passive end-effector. This limitation could be due to the lack of a common research framework, facilitating the integration of these two technologies. In this scenario, we proposed ROS-Neuro to overcome the aforementioned limitations by providing a common middleware between BMI and robotics. In this work, we present a working example of the potentialities of ROS-Neuro by describing a full closed-loop implementation of a BMI based on motor imagination. The paper shows the general structure of a closed-loop BMI in ROS-Neuro and describes the specific implementation of the packages related to the proposed motor imagery BMI, already available online with source codes, tutorials and documentations. Furthermore, we show two practical case scenarios where the implemented BMI is used to control a computer game or a telepresence robot with ROS-Neuro. We evaluated the performance of ROS-Neuro by ensuring comparable results with respect to a previous BMI software already validated. Results demonstrated the correct behavior of the provided packages.

Brain-Computer Interface systems have been widely studied and explored with adults demonstrating the possibility to achieve augmentative communication and control directly from the users' brain. Nevertheless, the study and the exploitation of the BCI in children seems to be limited. In this paper we propose and present for the first time a Brain-Robot Interface enabling children to mentally drive a robot. With this regards, we exploit the combination of a P300-based Brain-Computer Interface and a shared-autonomy approach to achieve a reliable and safe robot navigation. We tested our system in a pilot study involving five children. Our preliminary results highlight the advantages of using an accumulation framework, thanks to which the performance of the children reached the 81.67 % ± 12.7 on average in terms of accuracy. During the experiments, the shared-autonomy approach involved a low-level intelligent control on board of the robot to avoid obstacles, enabling an effective navigation also with a small number of commands.

Recent advances in the Brain-Machine Interface (BMI) and in the robotic fields allowed researchers to design a new generation of brain-actuated neuroprostheses to control a variety of assistive devices-ranging from powered wheelchairs to telepresence robots and robotic arms. However, the current interaction between BMI and robotic applications is still at its infancy. One of the reasons for the limited integration between these two technologies might be identified in the lack of a common research framework. In this scenario, ROS-Neuro may represent a solution to this challenge by providing a common middleware between BMI and robotics.In this work, we propose two packages for the ROS-Neuro middleware to acquire neural signals from different commercial acquisition devices and to store them into common data formats. We describe the usage and the main functionalities of both packages such as the provided nodes, the published and subscribed topics and the available services. Furthermore, the packages are available online together with source codes, tutorials and documentations. We evaluated the performances of the two packages to ensure the absence of delays or missing data with three commercial acquisition systems and in different operating configurations. Results demonstrated the correct behavior of the provided packages. The acquisition and recorder packages represent the first releases in the ROS-Neuro ecosystem and they aim at boosting both the shared development and the research activity in the multidisciplinary field of brain-actuated robots.

The identification of intentionally delivered commands is a challenge in Brain Computer Interfaces (BCIs) based on Sensory-Motor Rhythms (SMR). It is of fundamental importance that BCI systems controlling a robotic device (i.e., upper limb prosthesis) are capable of detecting if the user is in the so called Intentional Non-Control (INC) state (i.e., holding the prosthesis in a given position). In this work, we propose a novel approach based on the entropy of the Electroencephalogram (EEG) signals to provide a continuous identification of motion intention. Results from ten healthy subjects suggest that the proposed system can be used for reliably predicting motion in real-time at a framerate of 8 Hz with 80%±5% of accuracy. Moreover, motion intention can be detected more than 1 second before muscular activation with an average accuracy of 76%±11%.

This paper shows and evaluates a novel approach to integrate a non-invasive Brain-Computer Interface (BCI) with the Robot Operating System (ROS) to mentally drive a telepresence robot. Controlling a mobile device by using human brain signals might improve the quality of life of people suffering from severe physical disabilities or elderly people who cannot move anymore. Thus, the BCI user can actively interact with relatives and friends located in different rooms thanks to a video streaming connection to the robot. To facilitate the control of the robot via BCI, we explore new ROS-based algorithms for navigation and obstacle avoidance in order to make the system safer and more reliable. In this regard, the robot exploits two maps of the environment, one for localization and one for navigation, and both are used as additional visual feedback for the BCI user to control the robot position. Experimental results show a decrease of the number of commands needed to complete the navigation task, suggesting a reduction user's cognitive workload. The novelty of this work is to provide a first evidence of an integration between BCI and ROS that can simplify and foster the development of software for BCI driven robotics devices.