This study examines the privacy risks associated with the teleoperation of robots controlled via encrypted network communications. From the perspective of a network eavesdropper, we explore the potential to infer sensitive robotic actions by analyzing traffic metadata, such as packet timing, size, and direction. We investigate this threat using a smartphone’s Inertial Measurement Unit (IMU) to control a collaborative robotic arm via three joint-level modalities—position, velocity, and torque—to perform four distinct actions. First, empirical traffic analysis demonstrates that an adversary can identify robot actions with high accuracy using standard machine learning classifiers. Second, to determine whether the remaining classification errors stem from empirical model limitations or the structural constraints of the physical protocols, we apply a classifier-agnostic information-theoretic evaluation. Using mutual information, we derive Bayes optimal accuracy bounds and show that torque control leaks more deterministic information than suggested by empirical models, while velocity inherently exposes less information. Third, by prototyping a traffic padding defense, we evaluate the limitations of standard obfuscation against these structural leakages. Our findings highlight the necessity of information-theoretic bounds in privacy research and inform the design of secure robotic teleoperation APIs.

Shared autonomy in robot teleoperation can ease task completion and lower cognitive load for operators, by combining human intent with the autonomous capabilities of robots. As many manipulation control tasks involve the grasping of objects as the first step, augmenting assistance at this stage has the potential to improve user experience and task performance. This work discusses a new grasping assistance framework based on users’ intent signalling via eye gaze. Specifically, the eye gaze direction is retrieved from a virtual reality headset during the teleoperation. This information is used to automatically determine grasping locations on the target object, after which the grasping sequence is executed without the need to perform 1-to-1 motion mapping. The grasping assistance system is implemented using ROS2 to control a Kinova Gen 3 robotic manipulator, using a Meta Quest Pro virtual reality headset. A user study was performed with 30 participants using the developed system to compare the usability, workload, and performance of the grasping-assisted teleoperation with pure teleoperation (motion mapping). Results show that grasping assistance significantly reduces users’ workload, but also leads to lower performance metrics with respect to pure teleoperation.

Caregiving for older adults is an urgent global challenge, with many preferring to age in place rather than enter residential care. However, providing adequate home-based assistance is difficult, particularly in geographically vast regions. Teleoperated robots offer a promising solution, but conventional motion-mapping teleoperation imposes unnatural movement constraints, causing operator fatigue and reducing usability. This paper presents a novel teleoperation framework that leverages action recognition for intuitive remote robot control. A simplified Spatio-Temporal Graph Convolutional Network (S-ST-GCN) recognizes human actions and executes preset robot trajectories, eliminating the need for direct motion synchronization. A finite-state machine (FSM) further enhances reliability by filtering misclassified actions. Experiments demonstrate that the framework enables effortless operator movement and accurate robot execution. This proof-of-concept study highlights the potential of action-recognition-based teleoperation to help caregivers remotely assist older adults with daily activities. Future work will focus on improving the S-ST-GCN’s recognition accuracy and generalization, integrating advanced motion planning techniques for greater robot autonomy, and conducting user studies to evaluate telepresence and usability.

In this work, we present VAIRO, a Vision-based Adaptive Impedance-control RObotic framework for the purpose of manipulating soft materials, centered around the use case of rolling croissant dough for use in artisanal bakeries. Traditional automated processes for the industrial production of croissants consist of overly bulky equipment and fail to preserve the artisanal quality of hand-rolled croissants, with one of the major challenges being the high variability in the dough properties. VAIRO addresses these challenges by introducing a novel vision-based adaptive Cartesian impedance control strategy for collaborative robot arms to regulate rolling forces in real-time without the need for estimating the properties of the soft material. As such, VAIRO mimics the tactile adjustments made by human pastry chefs, ensuring consistent layer thickness and eliminating gaps. Using a Kinova Gen3 robotic arm and a custom-designed end-effector, we demonstrate that VAIRO can successfully manipulate various "doughs" without estimating any material properties. These results are promising and offer a cost-effective, small-scale alternative for local craft bakeries to leverage automation while maintaining high artisanal quality.

Anticipating a future scenario where the robot initiates its own actions and behaves voluntarily when collaborating with humans, our research focuses on human understanding and perception of unanticipated robot actions during physical human-robot interaction. While the current literature searches for key factors that make the human-robot collaboration successful, the question of how people experience the robot’s unanticipated action as cooperative or uncooperative seems to remain open. We designed a game-based experiment (N = 35) where the participant played a “catch-falling-coins” game by moving a robotic arm. Our experiment introduced unanticipated robot actions in an “active session” where the robot targeted higher-valued coins without first informing the participants. Through semi-structured interviews and statistical analysis of questionnaires (Big Five Personality Test, SAM, NARS and CH33), we examined the participants’ understanding of the robot’s “intention” and their positive or negative perception of the robot as cooperative or uncooperative. Among the participants who understood that the robot’s “intention” was to catch the higher-valued coins, the majority of them reported a positive perception of the robot (cooperative or helpful) while this was not the case among those who did not understand the robot’s intention. We also observed relevant relationships between some personality traits and a person’s understanding of the robot’s intention. Qualitative analysis of the interviews allowed us to structure the process of perception change during the game into three phases: confusion, investigation, and adaptation. We believe that our research contributes to the study of human perception, and particularly to the relationship between a human’s understanding of unanticipated robot actions and their positive or negative perception of the robot.

In a world designed for legs, quadrupeds, bipeds, and humanoids have the opportunity to impact emerging robotics applications from logistics, to agriculture, to home assistance. The goal of this survey is to cover the recent progress toward these applications that have been driven by model-based optimization for the real-time generation and control of movement. The majority of the research community has converged on the idea of generating locomotion control laws by solving an optimal control problem (OCP) in either a model-based or data-driven manner. However, solving the most general of these problems online remains intractable due to complexities from intermittent unidirectional contacts with the environment, and from the many degrees of freedom of legged robots. This survey covers methods that have been pursued to make these OCPs computationally tractable, with a specific focus on how environmental contacts are treated, how the model can be simplified, and how these choices affect the numerical solution methods employed. The survey focuses on model-based optimization while paving its way for broader combination with learning-based formulations to accelerate progress in this growing field.

Unanticipated physical actions from the robot on humans [active physical human–robot interaction (pHRI)] may be inevitable with the deployment of robots in human-populated environments. However, it is still unclear how humans would perceive such actions and how the robot should execute them in a physically and psychologically safe manner. The objective of this article is to explore the possibility of quantifying the humans’ physical and mental state during an active physical interaction with a robot, by means of a laboratory experiment. We hypothesize that the active robot actions could cause measurable alterations in users’ data, which could be related to their perceptions and personalities. In the experiment, the user plays a visual game using the robot, which has a hidden task that results in active physical actions on the user. We collect data from physical and physiological sensors, and the perceptions and personalities via questionnaires and a semi-structured interview. Statistical analysis and clustering of the data collected from a total of 35 participants showed the relationships between participants’ physical and physiological data and their age, gender, perception, and personalities. Further developments based on these exploratory outcomes can be used to implement an active pHRI controller that can account for both the physical and the mental state of users.

Studies have shown that the appearance and movements of home robots may play key roles in the impression and engagement of users, opposed to recent rises in the smart speaker market. In this research, we conduct a user experiment with the aim of clarifying the elements required to evaluate the human impression of a robot's movements, based on the hypothesis that adequate movements may lead to better impressions and engagement. We compare the impressions of participants who interacted with a robot with movements (behavior robot) and a robot without movements (non-behavior robot). Results show that when using the behavior robot, participants showed significantly higher values in their impressions of cheerfulness and sociability. Questionnaires about interaction revealed that personalization is also an important function for robots to make a good impression on humans.

Compliance is an important feature of human locomotion, where many studies have focused on the identification of stiffness parameters in order to build new machines, and stiffness is often assumed to be constant. However, it has been shown that during human walking, stiffness profiles are not constant in time and have high modulations in particular when changing walking phases. In order to understand how the constant stiffness assumption influences the walking gait and how much modulation is needed in walking mechanisms to reproduce natural gaits, in this work we focus on the analysis of joint and bi-articular stiffness profiles modulation and the influence of these modulations on the walking motions in different walking environments, i.e. level ground, slope and stairs. The analysis is carried out on periodic steps from motion capture data. mapped on a 14 segments human model with spring damper systems.

One of the biggest challenges of humanoid robots is to keep the balance at any moment, as they can be subject to different types of external perturbations. Therefore, push recovery is a relevant issue in humanoid robotics, which still represents an open challenge. In literature, the most used method is based on the capture point, where a reduced model of the robot is used to compute recovery motions. In this paper we want to explore the problem with a different approach, by using whole-body models combined with optimal control. The robot is subject to an external perturbation, which is taken into account in the system dynamics as continuous external force. Optimal control is used to generate whole-body recovery motions by using the whole-body dynamic model of the robot. Instead of the capture point criterion, we optimize for a stable motion that allows to perform recovery within one step, at the end of which the robot is in a state of zero velocity. We apply the method to the model of the humanoid robot HeiCub, where serial springs can be mounted in the knee and ankle pitch actuators to change them into Series Elastic Actuators (SEA). The recovery motion is computed for both the model with and without SEA.