Pedro Núñez

We present V-DART (VR-based Dual-Arm Robot Teleoperation), an extended virtual reality framework for the teleoperation of mobile dual-arm robotic systems, building upon our previous work on stationary manipulator control. The enhanced system leverages Unreal Engine 5.6.1 (UE5) for real-time visualization and interaction, introducing critical advancements for complex environments: the control of the robot’s base movement via both joystick input and direct mapping of the user’s physical position and orientation within the tracked space. This allows users to navigate the robot intuitively by simply moving their body, while maintaining precise control over both arms using VR (Virtual Reality) controllers. Furthermore, we introduce a robust data logging layer that records expert movements during teleoperation tasks. By capturing timestamped streams of user pose, controller inputs, and robot states that can be synchronized downstream, this module facilitates the creation of high-quality datasets from expert demonstrations. These datasets can be leveraged for machine learning applications, such as behavior cloning, reducing the need for extensive manual annotation. The system maintains bidirectional communication between the VR interface and the physical robot, ensuring responsive control and real-time feedback. We describe the architectural enhancements, user interaction design, and performance evaluation, demonstrating the viability of this unified approach for both teleoperation and data-driven robotics development.

We present a virtual reality (VR) framework for controlling dual-arm robotic manipulators through immersive interfaces, integrating both simulated and real-world platforms. The system combines the Webots robotics simulator with Unreal Engine 5.6.1 to provide real-time visualization and interaction, enabling users to manipulate each arm’s tool point via VR controllers with natural depth perception and motion tracking. The same control interface is seamlessly extended to a physical dual-arm robot, enabling teleoperation within the same VR environment. Our architecture supports real-time bidirectional communication between the VR layer and both the simulator and hardware, enabling responsive control and feedback. We describe the system design and performance evaluation in both domains, demonstrating the viability of immersive VR as a unified interface for simulation and physical robot control.

We present a virtual reality (VR) framework for controlling dual-arm robotic manipulators through immersive interfaces, integrating both simulated and real-world platforms. The system combines the Webots robotics simulator with Unreal Engine 5.6.1 to provide real-time visualization and interaction, enabling users to manipulate each arm’s tool point via VR controllers with natural depth perception and motion tracking. The same control interface is seamlessly extended to a physical dual-arm robot, allowing for teleoperation using the same VR environment. Our architecture supports real-time bidirectional communication between the VR layer and both the simulator and hardware, enabling responsive control and feedback. We describe the system design, and performance evaluation in both domains, demonstrating the viability of immersive VR as a unified interface for simulation and physical robot control.

The perception of 3D space by mobile robots is rapidly moving from flat metric grid representations to hybrid metric-semantic graphs built from human-interpretable concepts. While most approaches first build metric maps and then add semantic layers, we explore an alternative, concept-first architecture in which spatial understanding emerges from asynchronous concept agents that directly instantiate and manage semantic entities. Our robot employs two spatial concepts—room and door—implemented as autonomous processes within a cognitive distributed architecture. These concept agents cooperatively build a shared scene graph representation of indoor layouts through active exploration and incremental validation. The key architectural principle is hierarchical constraint propagation: Room instantiation provides geometric and semantic priors to guide and support door detection within wall boundaries. The resulting structure is maintained by a complementary functional principle based on prediction-matching loops. This approach is designed to yield an actionable, human-interpretable spatial representation without relying on any pre-existing global metric map, supporting scalable operation and persistent, task-relevant understanding in structured indoor environments.

The perception of 3D space by mobile robots is rapidly moving from flat metric 1 grid representations to hybrid metric-semantic graphs built from human-interpretable 2 concepts. While most approaches first build metric maps and then add semantic layers, 3 we explore an alternative concept-first architecture where spatial understanding emerges 4 from asynchronous concept agents that directly instantiate and manage semantic entities. 5 Our robot employs two spatial concepts—room and door—implemented as autonomous 6 processes within a cognitive distributed architecture. These concept agents cooperatively 7 build a shared scene graph representation of indoor layouts through active exploration 8 and incremental validation. The key architectural principle is hierarchical constraint 9 propagation: room instantiation provides geometric and semantic priors that constrain and 10 improve door detection within wall boundaries. The resulting structure is maintained by a 11 complementary functional principle: prediction-matching loops. This approach builds an 12 actionable, human-interpretable spatial representation without relying on a pre-existing 13 global metric map, enabling scalable operation and persistent, task-relevant understanding 14 in structured indoor environments.

The main challenge addressed by this project is population ageing, a pressing issue for public policies in both Spain and Portugal. This phenomenon is particularly prominent in the regions involved in the project (EuroACE: Extremadura, Central Portugal, and Alentejo), which share common characteristics: low population density and a high rate of ageing. Thus, the project tackles shared territorial challenges in the Spain–Portugal cross-border area. Objectives: The primary objective of the project is to improve the quality of life of older adults by promoting autonomy using innovative technologies and cross-border cooperation. Methods: The promotion of physical activity, cognitive and socio-emotional stimulation, as well as the use of Information and Communication Technologies—such as robotics, serious games, and brain-computer interfaces—will be the main tools developed throughout the project. Through the development and implementation of these solutions and the application of technical knowledge, the cross-border cooperation is expected to contribute to addressing the demographic challenge of an ageing population. Results: The project is expected to have a positive impact on the EuroACE region, involving around 200 professionals in the implementation of the developed solutions with over 1,000 older adults. Through the project’s beneficiary partners, the results are also expected to impact over 50 institutions and companies in the business ecosystem. Conclusion: The EuroAGE+ Network will be essential in fostering research and innovation in the field of health and healthy ageing, improving quality of life and extending the healthy life expectancy of the population.

The integration of robots into social environments necessitates their ability to interpret human intentions and anticipate potential outcomes accurately. This capability is particularly crucial for social robots designed for human care, as they may encounter situations that pose significant risks to individuals, such as undetected obstacles in their path. These hazards must be identified and mitigated promptly to ensure human safety. This paper delves into the artificial theory of mind (ATM) approach to inferring and interpreting human intentions within human–robot interaction. We propose a novel algorithm that detects potentially hazardous situations for humans and selects appropriate robotic actions to eliminate these dangers in real time. Our methodology employs a simulation-based approach to ATM, incorporating a “like-me” policy to assign intentions and actions to human subjects. This strategy enables the robot to detect risks and act with a high success rate, even under time-constrained circumstances. The algorithm was seamlessly integrated into an existing robotics cognitive architecture, enhancing its social interaction and risk mitigation capabilities. To evaluate the robustness, precision, and real-time responsiveness of our implementation, we conducted a series of three experiments: (i) A fully simulated scenario to assess the algorithm’s performance in a controlled environment; (ii) A human-in-the-loop hybrid configuration to test the system’s adaptability to real-time human input; and (iii) A real-world scenario to validate the algorithm’s effectiveness in practical applications. These experiments provided comprehensive insights into the algorithm’s performance across various conditions, demonstrating its potential for improving the safety and efficacy of social robots in human care settings. Our findings contribute to the growing research on social robotics and artificial intelligence, offering a promising approach to enhancing human–robot interaction in potentially hazardous environments. Future work may explore the scalability of this algorithm to more complex scenarios and its integration with other advanced robotic systems.