The integration of robots into human-shared environments has driven advancements in social robotics and natural Human-Robot Interaction. A key challenge in this field is to enable robots to interpret and respond to social cues, ensuring fluid, context-aware, and socially acceptable interactions. To address this, we propose a two-layer architecture for social navigation. The high-level reasoning layer utilizes Large Language Models to interpret contextual and environmental cues, generating constraints for navigation. These constraints are then enforced by the low-level layer, which employs Control Barrier Functions to ensure smooth and socially compliant robot movements. We validate our approach in a dynamic simulation environment, demonstrating effective constraint enforcement and socially acceptable navigation behavior.

This article proposes a shared-control teleoperation architecture for robot manipulators transporting an object on a tray. Differently from many existing studies about remotely operated robots with firm grasping capabilities, we consider the case in which, in principle, the object can break its contact with the robot end-effector. The proposed shared-control approach automatically regulates the remote robot motion commanded by the user and the end-effector orientation to prevent the object from sliding over the tray. Furthermore, the human operator is provided with haptic cues informing about the discrepancy between the commanded and executed robot motion, which assist the operator throughout the task execution. We carried out trajectory tracking experiments employing an autonomous 7-degree-of-freedom (DoF) manipulator and compared the results obtained using the proposed approach with two different control schemes (i.e., constant tray orientation and no motion adjustment). We also carried out a human-subjects study involving 18 participants in which a 3-DoF haptic device was used to teleoperate the robot linear motion and display haptic cues to the operator. In all experiments, the results clearly show that our control approach outperforms the other solutions in terms of sliding prevention, robustness, commands tracking, and user’s preference.

This paper addresses the problem to simulate ARVA sensors using ROS and Gazebo. ARVA is a French acronym which stands for Appareil de Recherche de Victims en Avalanche and represents the forefront technology adopted in Search & Rescue operations to localize victims of avalanches buried under the snow. The aim of this paper is to describe the mathematical and theoretical background of the transceiver, discussing its implementation and integration with ROS allowing researchers to develop faster and smarter Search &Rescue strategies based on ARVA receiver data. To assess the effectiveness of the proposed sensor model, We present a simulation scenario in which an Unmanned Aerial Vehicle equipped with the transceiver sensor performs a basic S&R pattern using the output of ARVA system.

Cobots (Collaborative robots) are increasingly employed in industrial workplaces, assisting human operators in decreasing the weight and the repetitiveness of their activities. In order to promote the acceptance of cobots, novel interaction paradigms enabling intuitive collaboration between humans and robots are needed. In this work, we propose a variable admittance control framework based on virtual fixtures, in which the damping and the stiffness of the admittance controller are on-line adjusted to increase the effectiveness of the co-manipulation task. Virtual paths are generated to support and guide the operator during collaborative task execution, providing him/her an over-responsive compliant system when the robot is far from the targets and a precise heavy tool in their proximity. The proposed approach has been compared with a fixed admittance controller in an industrial use case consisting of a human operator interacting with a Kuka LBR IIWA arm. The collected results demonstrate the effectiveness of the proposed approach.

We present a novel multimodal interaction framework supporting robust human-robot communication. We consider a scenario where a human operator can exploit multiple communication channels to interact with one or more robots in order to accomplish shared tasks. Moreover, we assume that the human is not fully dedicated to the robot control, but also involved in other activities, hence only able to interact with the robotic system in a sparse and incomplete manner. In this context, several human or environmental factors could cause errors, noise and wrong interpretations of the commands. The main goal of this work is to improve the robustness of human-robot interaction systems in similar situations. In particular, we propose a multimodal fusion method based on the following steps: for each communication channel, unimodal classifiers are firstly deployed in order to generate unimodal interpretations of the human inputs; the unimodal outcomes are then grouped into different multimodal recognition lines, each representing a possible interpretation of a sequence of multimodal inputs; these lines are finally assessed in order to recognize the human commands. We discuss the system at work in a case study in which a human rescuer interacts with a team of flying robots during Search & Rescue missions. In this scenario, we present and discuss real world experiments to demonstrate the effectiveness of the proposed framework.

We present a system suitable for human multi-robot interaction that supports the operator in the robot selection process. The proposed framework allows a human to issue commands to a robotic team without an explicit robot selection in so enabling a fluent interaction. This work is framed in the operative context of the SHERPA project [1], which proposes the deployment of a robotic platform for Search & Rescue in an alpine scenario and assumes the presence of a human rescuer that can orchestrate the robots operations with multimodal commands. In this context, implicit robot selection is mainly motivated by fast communication and the difficulties to distinguish different robots of similar shape in a hazardous environment and in adverse weather conditions. In the proposed approach, each robot of the team can evaluate the probability to be referred in an incomplete command, considering its actual capabilities along with geometrical and contextual information. We describe the overall system architecture focusing on the human intention recognition process. The proposed framework is trained and evaluated in a simulated case study.

A human-robot interaction system should be capable of adapting the execution of cooperative plans with respect to complex human activities and interventions. In this paper, we present an integrated framework that exploits attentional supervision and contention scheduling to combine human-aware planning, plan execution, and natural human-robot interaction. Specifically, in the proposed approach, hierarchical cooperative plans are exploited as top-down attentional guidance for the robotic executive system, which can flexibly orchestrate the task activities while reacting to environmental stimuli and human behaviors. We describe the overall framework discussing some case studies in human-robot collaborative scenarios.

We present a multimodal attentional interface suitable for a human operator that monitors and controls the activities of a team of drones during search and rescue missions. We consider a scenario where the operator is a component of the rescue team, hence not fully dedicated to the robots, but only able to interact with them with sparse and incomplete commands. In this context, an adaptive interface is needed to support the user situation awareness and to enable an effective interaction with the drones. In this work, we propose a multimodal attention-based interface designed for this domain. This framework is to filter the information flow towards the operator selecting and adapting the communication mode according to the context and the human state. We illustrate the features of the adaptive system along with an initial assessment in a simulated scenario.

An estimator of external generalized forces (force plus moments) acting on aerial platforms, and based on the momentum of the mechanical system, is proposed for the control of VToL UAVs together with a hierarchical architecture separating the translational and rotational dynamics of the vehicle. The closed-loop system equations are shaped as mechanical impedances, programmable through the controller gains, and forced by the residuals given by the estimation error. This arrangement allows the VToL UAVs to perform hovering and tracking tasks without a precise knowledge of the vehicle dynamics and in presence of external disturbances and unmodeled aerodynamic effects. Experiments are presented to evaluate the performance of the proposed control design.

We present an approach to mixed initiative control for unmanned aerial vehicles (UAVs) where sliding autonomy is supported by mixed-initiative planning and haptic feedback. In the proposed framework, we assume that an autonomous system can plan and execute robotic tasks while a human operator can provide interventions when necessary receiving a force feedback. The haptic feedback is associated with the sensation about how the system is diverging from the planned operations. We tested the system at work in virtual and real environments considering simple navigation tasks. We compared the performance of human operators with or without the assistance of the force feedback. The collected results support the hypothesis that the proposed approach enables effective and intuitive mixed-initiative control.