ABSTRACT The selective migration of young people to urban centres has resulted in an ageing workforce within the Brazilian coffee sector, creating a growing demand for solutions that aid producers in crop management routines. Coffee production, the fourth largest in terms of gross national agricultural revenue, can benefit from technological support. In this context, this research investigates social perceptions of a prototype of social robots by agricultural producers. The study involved a sample of 28 rural producers and stakeholders, stratified by age into two independent groups: 18–49 years () and 50+ years (). Participants evaluated the robot using the Robotic Social Attributes Scale (RoSAS), enabling a detailed look at social perceptions. The analysis yielded four distinct user profiles: (1) Engaged Supporters, (2) Pragmatic Users, (3) Innovation Sceptics and (4) Confident Adopters. Furthermore, correlation analysis highlighted strong links between perceived social attributes (e.g., high correlation between trustworthy and competent). Crucially, non‐parametric testing revealed a statistically significant difference across age groups regarding the robot's perceived organic nature (). These findings indicate a clear generational shift in social robot acceptance, underscoring the need for age‐sensitive design in future assistive robotics for agriculture.

Human–Robot Interaction (HRI) has emerged as a pivotal domain in robotics, centering on the interplay and collaboration between humans and robots to achieve complex tasks. Effective communication is a cornerstone of successful HRI, facilitating the exchange of critical information essential for joint decision-making and task execution. This article explores the intricate dynamics of collaborative communication in physical HRI (pHRI), specifically focusing on non-verbal cues. Within HRI, we assert that collaboration fundamentally hinges on communication, wherein agents share information to achieve common objectives. Information theory provides a rigorous mathematical framework for quantifying the flow of information within communicating agents. It serves as a unifying framework for evaluating the dynamic interplay of various communication channels in pHRI. This study introduces a non-parametric approach based on information entropy to assess communication between agents in pHRI scenarios and detect important behaviors, such as information flow, leadership, and coupling. Through a comprehensive experimental setup involving collaborative catching tasks, we demonstrate the versatility and applicability of the proposed methodology.

In physical human-robot interaction, force feedback has been the most common sensing modality to convey the human intention to the robot. It is widely used in admittance control to allow the human to direct the robot. However, it cannot be used in scenarios where direct force feedback is not available since manipulated objects are not always equipped with a force sensor. In this work, we study one such scenario: the collaborative pushing and pulling of heavy objects on frictional surfaces, a prevalent task in industrial settings. When humans do it, they communicate through verbal and non-verbal cues, where body poses, and movements often convey more than words. We propose a novel context-aware approach using Directed Graph Neural Networks to analyze spatiotemporal human posture data to predict human motion intention for non-verbal collaborative physical manipulation. Our experiments demonstrate that robot assistance significantly reduces human effort and improves task efficiency. The results indicate that incorporating posture-based context recognition, either together with or as an alternative to force sensing, enhances robot decision-making and control efficiency.

To enable a comprehensive human-robot interaction, it is essential to refer to human-human collaboration and decode complex non-verbal communication aspects that are essential for adaptive decision-making and task success. Indeed, for a robust collaboration, it is useful to understand the intricacies and complexities of human communication during human-human interaction and to compare it with the human-robot interaction case. We study this communication exchange and information flow by evaluating the leader/follower behavior during physical interaction with different agents and different control types, focusing on non-verbal cues, to identify collaborative or competitive attitudes. To achieve this, we consider a dynamic task of collaboratively catching a falling object, which, by its nature, favors non-verbal communication channels. Multiple subjects performed the same task with different collaborative agents (i.e., human and robot) and with different control modalities, to evaluate the leadership roles and their implication on task success (successfully catching the object while minimizing impact forces). We analyze how the impact force minimization induced by the velocity matching optimal planner affects the catching success rate. The information flow is analyzed, and the leadership roles are identified. Further qualitative data is gathered from questionnaires and compared with respect to the analytic results.

This paper proposes a combined optimization and learning method for impact-friendly, non-prehensile catching of objects at non-zero velocity. Through a constrained Quadratic Programming problem, the method generates optimal trajectories up to the contact point between the robot and the object to minimize their relative velocity and reduce the impact forces. Next, the generated trajectories are updated by Kernelized Movement Primitives, which are based on human catching demonstrations to ensure a smooth transition around the catching point. In addition, the learned human variable stiffness (HVS) is sent to the robot's Cartesian impedance controller to absorb the post-impact forces and stabilize the catching position. Three experiments are conducted to compare our method with and without HVS against a fixed-position impedance controller (FP-IC). The results showed that the proposed methods outperform the FP-IC while adding HVS yields better results for absorbing the post-impact forces.

The field of physical human-robot interaction has dramatically evolved in the last decades. As a result, the robotic system's requirements have become more challenging, including personalized behavior for different tasks and users. Various machine learning techniques have been proposed to give the robot such adaptability features. This paper proposes a model-based evolutionary optimization algorithm to tune the apparent impedance of a wrist rehabilitation device. We used passivity to define boundaries for the possible controller outcomes, limiting the shared autonomy of the robot and ensuring the coupled system stability. The experiment consists of a hardware-in-the-loop optimization and a one-degree-of-freedom robot used for wrist rehabilitation. Experimental results showed that the proposed technique could generate customized passive impedance controllers for three subjects. Furthermore, when compared with a constant impedance controller, the method suggested decreased in 20% the root mean square of interaction torques while maintaining stability during optimization.

Although the design of legged robots may be dependent on the application, all of them share the need to safely deal with physical interaction with the environment in every step they take. Impedance controllers have been applied with success to handle contact, and some authors applied the concept of passivity to guarantee stability during the interaction. Whereas previous studies on the passivity of legged robots considered aspects such as inner force loop gains and actuation bandwidth influence on the Z-width (i.e. the range of renderable passive impedances), they did not take into account the role of the kinematic configuration of the leg on the stability of the interaction. Thus, in this work we present a systematic analysis of the effects of joint positions on the passivity conditions of a robotic leg and show that this is a very relevant aspect that may seriously affect the stability and passivity of an impedance controller. By analyzing a linearized model of the leg via its Nyquist plots and the respective Z-width diagrams, we were able to determine what joint configurations within the leg workspace are more suitable to physically interact with the environment or people.

Fasteners assembly automation in different industries require flexible systems capable of dealing with faulty situations. Fault detection and isolation (FDI) techniques are used to detect failure and deal with them, avoiding losses on parts, tools or robots. However, FDI usually deals with the faults after or at the moment they occur. Thus, we propose a method that predicts potential failures online, based on the forces and torques signatures captured during the task. We demonstrate the approach experimentally using an industrial robot, equipped with a force-torque sensor and a pneumatic gripper, used to align and thread nuts into bolts. All effort information is fed into a supervised machine learning algorithm, based on a Convolutional Neural Network (CNN) classifier. The network was able to predict and classify the threading task outcomes in 3 groups: mounted, not mounted or jammed. Our approach was able to reduce in 10.9% the threading task execution time when compared to a reference without FDI, but had problem to predict jammed cases. The same experiment was also performed with other two additional learning algorithms, and the results were systematically compared.

Impedance control is commonly implemented for robotic contact applications. Its performance is a function of the dynamic coupling between the environment and the robot's impedance controller: inertia, stiffness and damping. An interaction task may be considered successfully accomplished when the elected performance criteria, such as rise time, overshoot and accommodation are achieved, all in addition to guaranteeing stability. The selection and online changes of impedance parameters for such purpose is considered challenging for practical applications, given the non modeled dynamics of the robot and its intrinsic impedance, uncertainties in the dynamics of environment and tool. This study proposes the usage of a multi-objective genetic algorithm to obtain enhanced impedance controller gains, with the purpose of providing impedance control adaptability to a robot in a real physical interaction application. In this context, a one-degree-of-freedom mechanical contact model is proposed to obtain the values of the objective function. A genetic algorithm is used to obtain the best gains, as this is a nonlinear task. Simulation and experimental results are presented and discussed validating the system performance, which has shown a convergence of less than 6 generations. Tendencies to convergence of controller parameters were observed and discussed.