University students often face unique challenges in self-regulated learning, such as low motivation, emotional fatigue, and academic stress. While socially interactive robots hold promise as study companions, many current systems lack emotional expressivity and contextual relevance at the university level. This paper presents the design and simulation of a multimodal emotional expression system for the Robot Study Companion (RSC), aimed at supporting students’ emotional engagement and learning motivation.Six target emotions: joy, caring, pride, anger, fun, and surprise, were selected for their documented positive effects on learner behavior and mapped to common academic scenarios. Using a digital twin framework, emotional expressions were developed across motion, facial design, color, and voice. This simulation enabled rapid, iterative design refinement and supports culturally adaptive testing across geographic contexts.Rather than focusing solely on traditional learning outcomes, this work emphasizes students’ affective responses, impressions of the robot, and motivational impact. The project lays the foundation for cross-cultural user studies, beginning in Guyana and Estonia, which will evaluate emotional recognition, user experience, and the effectiveness of expressive behavior in enhancing academic engagement. These insights aim to inform the future development of emotionally intelligent and culturally responsive robotic companions for higher education.

To facilitate continuous development of novel HRI systems, it is beneficial to have tools that enable quick adjustments, flexibility, or re-invention of the human interfaces when system requirements change due to updates in the state-of-the-art, application domain, etc. Thus, modularity is a key design principle which promotes software reuse and scalability, and reduces development time and cost. Hence, a robot’s autonomous capabilities should not depend on the command interface and should be decoupled via a common format that possesses the descriptive capabilities for outlining tasks and has a sensible syntax for HRI. In this paper, we propose the Unified Meaning Representation Format (UMRF) , which provides the syntax and semantics for passing both simple and complex commands modelled as control flow graphs. UMRF is a standalone meaning representation container that supports embedding other meaning representation formalisms, such as predicate-argument semantics and graphical meaning representation formats, making it adoptable as a standard task description format for semi-autonomous systems in HRI domains. In this article, we define the UMRF syntax and semantics, summarize its unique aspects relative to related task description formats, and demonstrate its descriptiveness by navigating a robot via concurrent (e.g., gestures and speech) and interchangeable input systems (e.g., Google Assistant, Amazon Alexa).

With the imminent addition of autonomous vehicles to traffic, it is becoming more vital to look at different alternatives to non-verbal communication between the driver and the pedestrian, so that the pedestrian would understand the intentions of autonomous vehicles. The aim of this paper is to evaluate different approaches to communicate the intent of an autonomous vehicle. A survey study was conducted among Estonian people to analyze their understanding of animations of more prominent explicit external interaction modalities. The study revealed that participants may not understand the vehicle intent if they have no prior knowledge about the displayed signals.

For humans and robots to share space and cooperate, communication is essential. In the communication between a human and a robot it is important for the machine to understand the human but also for the human to understand the information conveyed by the robot. This requires, that the robot would be able to convey its intent to humans in an understandable way. The aim of this article is to evaluate different approaches to communicate the intent of a machine. A survey study was conducted to analyze the understanding of pseudo-emotional states and other status expressions of a robot. The study of 171 responses revealed that participants are less likely to correctly interpret the emotions and/or status of a machine if only robot’s eyes are used to express the emotions and system status.

Most commercially available fixed-wing aerial vehicles (FWV) can carry only small, lightweight computing hardware such as Jetson TX2 onboard. Solving non-linear trajectory optimization on these computing resources is computationally challenging even while considering only the kinematic motion model. Most importantly, the computation time increases sharply as the environment becomes more cluttered. In this paper, we take a step towards overcoming this bottleneck and propose a trajectory optimizer that achieves online performance on both conventional laptops/desktops and Jetson TX2 in a typical urban environment setting. Our optimizer builds on the novel insight that the seemingly non-linear trajectory optimization problem for FWV has an implicit multi-convex structure. Our optimizer exploits these computational structures by bringing together diverse concepts from Alternating Minimization, Bregman iteration, and Alternating Direction Method of Multipliers. We show that our optimizer outperforms the state-of-the-art implementation of sequential quadratic programming approach in optimal control solver ACADO in computation time and solution quality measured in terms of control and goal reaching cost.

Trajectory optimization problems under affine motion model and convex cost function are often solved through the convex-concave procedure (CCP), wherein the non-convex collision avoidance constraints are replaced with its affine approximation. Although mathematically rigorous, CCP has some critical limitations. First, it requires a collision-free initial guess of solution trajectory which is difficult to obtain, especially in dynamic environments. Second, at each iteration, CCP involves solving a convex constrained optimization problem which becomes prohibitive for real-time computation even with a moderate number of obstacles, if long planning horizons are used.In this paper, we propose a novel trajectory optimizer which like CCP involves solving convex optimization problems but can work with an arbitrary initial guess. Moreover, the proposed optimizer can be computationally upto a few orders of magnitude faster than CCP while achieving similar or better optimal cost. The reduced computation time, in turn, stems from some interesting mathematical structures in the optimizer which allows for distributed computation and obtaining solutions in symbolic form. We validate our claims on difficult benchmarks consisting of static and dynamic obstacles.

One of the main difficulties in teleoperated systems is providing an operator with sufficient Situational Awareness (SA). This paper introduces three open-source packages that improve the operator's SA using the Robot Operating System (ROS). The first package-rviz_textured_sphere-allows rendering panospheric camera outputs as spherical images in the ROS visualization software RViz. A system where the visualization of this spherical data using an open-source virtual reality (OSVR) headset in the ROS framework is achieved with the second package: rviz_plugin_osvr. Finally, the third package-pointcloud_painter-projects spherical data onto a 3D depth cloud scan of the scene generated from a rotating lidar. This package outputs a XYZRGB pointcloud that can be visualized either in RViz or using the virtual reality headset. Together, these technologies address the wider issue of limited SA in robotics and represent a substantial advancement in the environment visualization capabilities available to open-source robotics developers.

Telerobotics (i.e., remote-controlling robots) is highly attractive for tasks in potentially dangerous situations, e.g., search and rescue, space exploration, and handling hazardous materials. However, when telerobots are deployed to complete tasks, the human operator needs to develop task plan and figure out how to execute it using the available control interface. Inappropriate controls can lead to excessive cognitive load and long task completion times. If the human operator can interact with the robot in an intuitive way, he or she can focus more on the task. For that reason, we have designed a human-centered control interface that allows the operator to modify the user perspective, command via hand gestures and natural language, and scale human input motion to any suitable range on the robot. The interface consists of a Leap Motion Controller for hand tracking, microphone for speech detection, and a simple turn knob for varying the scaling factor between the human and robot motions. The teleoperator software utilizes the Robot Operating System (ROS) which enables open-source development and hardware agnosticism. In this paper we demonstrate the feasibility of the proposed system by executing a high-precision task of threading a needle. Furthermore, we present results from a usability study in where people were asked to complete high-precision tasks with both the developed human-centered gestural control input and a conventional functionality-centered drag-and-drop interface.