United States

This paper proposes an energy consumption estimation in the trajectory planning task of an industrial robot (IR) arm manipulator. Human-robot collaborative scheduling has become integral to modern manufacturing. Effective scheduling of human-robot cooperation is crucial for enhancing production efficiency. However, energy-efficient trajectory planning in human-robot interaction remains an unexplored area. To address this, a 6-DoF (Degree of Freedom) robot arm, available in the laboratory, was utilized to assess the energy consumption of this specific robot type. However, his approach can be applied to any robot, whether it&#39;s a human-robot collaboration, a mobile robot, or any other articulated robot arm manipulator. For this goal the electrical and mechanical losses of a 6-DoF robot arm manipulator from the company Universal Robots is modeled and its power losses cost function (PLCF) is derived correspondingly to calculate the electromechanical energy losses. The objective of this paper is to estimate the energy losses in a trajectory planning task in cartesian space for a tool center point (TCP) of the robot, starting from an initial point and resting at a final point. This goal is achieved by examining and analyzing the kinematics and dynamics followed by measuring the velocity and acceleration related terms of each motor at each joint of the UR3e robot arm in a laboratory setup. The results estimate the changes of energy losses in the overall trajectory following of the robot manipulator and presents the results of the estimated and measured reduction terms.

IEEE RO-MAN 2024

On Experimental Energy Consumption Estimation of a 6-DoF Industrial UR3e Robot Arm Manipulator in Trajectory Planning

electromechanical losses

motion planning

power consumption

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. This theme also touches upon people's expectations and perceptions of robots, as well as other social, cognitive and cultural factors that will influence human-robot interactions. We will have a special session/track on this topic, invite targeted submissions and include plenary speakers who are specialist in this area. We sincerely hope that this focus will help enlighten the design-thinking of all of us as we fabricate and program our own various systems.

To access RO-MAN Virtual 2021, [please register here.](https://ro-man2021.org/registration/)

IEEE RO-MAN 2021

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. 

This paper proposes an energy consumption estimation in the trajectory planning task of an industrial robot (IR) arm manipulator. Human-robot collaborative scheduling has become integral to modern manufacturing. Effective scheduling of human-robot cooperation is crucial for enhancing production efficiency. However, energy-efficient trajectory planning in human-robot interaction remains an unexplored area. To address this, a 6-DoF (Degree of Freedom) robot arm, available in the laboratory, was utilized to assess the energy consumption of this specific robot type. However, his approach can be applied to any robot, whether it's a human-robot collaboration, a mobile robot, or any other articulated robot arm manipulator. For this goal the electrical and mechanical losses of a 6-DoF robot arm manipulator from the company Universal Robots is modeled and its power losses cost function (PLCF) is derived correspondingly to calculate the electromechanical energy losses. The objective of this paper is to estimate the energy losses in a trajectory planning task in cartesian space for a tool center point (TCP) of the robot, starting from an initial point and resting at a final point. This goal is achieved by examining and analyzing the kinematics and dynamics followed by measuring the velocity and acceleration related terms of each motor at each joint of the UR3e robot arm in a laboratory setup. The results estimate the changes of energy losses in the overall trajectory following of the robot manipulator and presents the results of the estimated and measured reduction terms.

technical paper

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Dear Colleagues and Friends,

The 33rd IEEE International Conference on Robot and Human Interactive Communication, IEEE RO-MAN 2024, was held at Pasadena Convention Center in Pasadena, USA.

The theme of the IEEE RO-MAN 2024 was **“Embracing Human-Centered HRI”**.

With the continuous technological effort of understanding human needs, creating human-desired robotic systems, and developing real human user experiences, we are marching together towards building a promising and excellent human quality of life, all for our wellness and our next generation.

The conference provided a forum for researchers and practitioners to discuss a variety of topics related to the broad theme of Robot and Human Interactive Communication involving theories, methodologies, technologies, empirical and experimental studies, and generated more insights for the future of Robot and Human Interactive Communication!

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The 33rd IEEE International Conference on Robot and Human Interactive Communication was held in Pasadena, California, USA. The theme of the IEEE RO-MAN 2024 was “Embracing Human-Centered HRI”.

Deploying robots in human-shared environments requires a deep understanding of how nearby agents and objects interact. Employing causal inference to model cause-and-effect relationships facilitates the prediction of human behaviours and enables the anticipation of robot interventions. However, a significant challenge arises due to the absence of implementation of existing causal discovery methods within the ROS ecosystem, the standard de-facto framework in robotics, hindering effective utilisation on real robots. To bridge this gap, in our previous work we proposed ROS-Causal, a ROS-based framework designed for onboard data collection and causal discovery in human-robot spatial interactions. In this work, we present an experimental evaluation of ROS-Causal both in simulation and on a new dataset of human-robot spatial interactions in a lab scenario, to assess its performance and effectiveness. Our analysis demonstrates the efficacy of this approach, showcasing how causal models can be extracted directly onboard by robots during data collection. The online causal models generated from the simulation are consistent with those from lab experiments. These findings can help researchers to enhance the performance of robotic systems in shared environments, firstly by studying the causal relations between variables in simulation without real people, and then facilitating the actual robot deployment in real human environments.<br> ROS-Causal: https://lcastri.github.io/roscausal

Experimental Evaluation of ROS-Causal in Real-World Human-Robot Spatial Interaction Scenarios

As collaborative robots become more common in manufacturing scenarios and adopted in hybrid human-robot teams, we should develop new interaction and communication strategies to ensure smooth collaboration between agents. In this paper, we propose a novel communicative interface that uses Mixed Reality as a medium to perform Kinesthetic Teaching (KT) on any robotic platform. We evaluate our proposed approach in a user study involving multiple subjects and two different robots, comparing traditional physical KT with holographic-based KT through user experience questionnaires and task-related metrics.

Kinesthetic Teaching in Robotics: A Mixed Reality Approach

Recently, electroencephalographic (EEG) signals have been used to enhance Human-Robot Interaction (HRI). In particular, Error-Related Potentials (ErrPs) have been exploited since very few years. These potentials are evoked when there is a mismatch between the command given by the subject and the movements of the robot, or if the users expectation is different from the robot or other human behavior. These signals can be used to improve and customize the robot system, as feedback to better adapt the robot to human needs. This work aims to investigate and detect the ErrPs during different interaction tasks. We set up an experiment divided into five different tasks, where every task has 120 events with a 25%- 35% probability of error. The robot used in the experiment is a Baxter robot and the commands from the subject to the robot are sent in two different ways: with a keyboard or with a motion capture device. This work aims to reproduce a simplified teleoperated pick and place task. However, the achieved results do not allow to correctly identify the ErrPs, but exhibit only some minor differences between trials with and without errors. Hence, we here analyze the reasons behind such negative results, focusing on the challenges of the structure and the setup of the experiment. We analyze the possible problems and provide some recommendations to overcome them in similar use cases.

Challenges in Detecting and Analyzing EEG Error-Related Potentials: Lessons from a Case Study in HRI

Functional electrical stimulation is a promising technique for restoring arm function to those with paralysis from a high spinal cord injury. While simple controllers are easy to implement, model-based controllers are likely better equipped to leverage the arm's kinematic and dynamic complexity, particularly for the high variations associated with functional arm movement. One modelling technique for a model-based controller is Gaussian Process Regression. Previous simulation work has shown promise leveraging whole-arm error data to identify the arm's various subsystems, but used perfect simulated data. We asked caregivers to correct a robotic arm's movement as simulated muscles generated torque. The simulated muscles were controlled as if they were electrically stimulated human arm muscles. This study demonstrates non-expert caregivers' ability to collect this error data via whole-arm corrections, and provides insight into their ability to improve arm subsystem models made with Gaussian Process Regression. Despite significant error in caregivers' ability to provide force corrections to hold the robot in a static configuration, these corrections were leveraged to significantly improve muscle models; the muscles that improved the most were the ones primarily used to move the physiologically actuated robot.

Non-Expert Caregivers to Improve the Identification of a Physiologically Actuated Robot

In this work, we present a learning from demonstration solution for automating a surgical blunt dissection task. In addition to learning motion trajectories, our goal is to learn variable impedance behaviors that enable the robot to interact safely and compliantly during the task. To that end, we propose a teaching interface using bilateral teleoperation, which allows the natural transfer of human motions and impedance behaviors skills to robots. The demonstrated profiles are captured with Dynamic Movement Primitives and Gaussian Mixture Models, which subsequently provide the robot a reference motion plan, and a stiffness adaptation policy, during physical interaction. Experimental validation on real robot hardware shows the effectiveness of the proposed approach in terms of ensuring successful task execution, as well as safety compared to stiff high-gain control.

Learning from Demonstration of Robot Motions and Stiffness Behaviors for Surgical Blunt Dissection

Social robots and virtual agents can provide a low-threshold social interface for the independent self-health monitoring of older adults, thereby supporting their self-care and empowering them to take charge of their own health. In this work, we present a study on the interface preference of 35 older adults for a health monitoring system which they will be evaluating for eight weeks in their own homes. We found that participants who prefer the social robot have a higher affinity for technology interaction (ATI) score and are more likely to use assistance systems in their daily lives, while participants with a slightly lower ATI score prefer the virtual agent. Participants cited more personality and an interest in robots as reasons for the robot preference, and space and flexibility as reasons to prefer the virtual agent. These results underscore the importance of a personalized introduction of social technologies for health monitoring into the daily lives of older adults.

To Be or Not to (Physically) Be? A Study on Preferences in Embodied Socially Interactive Agents for Health Monitoring of Older Adults

Past research with participants in the United States showed that, in competitive group tasks, they have more positive attitudes and behaviors toward robots on their team over humans in another team. Here we present a study in which two Japanese students and two robots, placed in a randomly assigned group, compete with another student-and-robot team in a digital game. We explored participants moral behavior towards the robots, measured through their assignment of loud noise blasts to human and robot participants, and their perceptions of and attitudes towards the robots. We then compared this data to that which was collected within the United States. Results indicated that participants in Japan favored their ingroup humans and robots over outgroup agents and differentiated ingroup members more than outgroup members, as within the US. Japanese participants also anthropomorphized robots more than US participants and treated them more positively than US participants.

Ties That Bind: Group Effects in Human-Robot Team Interaction in Japan and the United States

Though some matters of consensus have begun to crystallize, scholars in human-robot interaction have thus far reasoned about artificial identity under many different definitions. Many of these seemingly disparate perspectives may, however, be unified into one coherent model through a synthesis of contemporary scientific and Buddhist philosophy of identity. Under this model, artificial and human identity are modeled equivalently under an assumption that there is no &quot;unchanging essence&quot; which constitutes an agent's identity, but rather that identity may be defined as the sum of overlapping aggregates subject to change through time. The model reckons with the idea that much of what is conceived of as identity may be arbitrarily ascribed, artificial boundaries, but that these boundaries often constitute substantial social and psychological realities. This thinking is congruent with contemporary philosophical perspectives across disciplines from biology to cognitive science. The model may serve as a useful tool for reasoning about identity in complex, dynamic situations and provide a firm foundation for work which utilizes artificial identity. The model may even offer one or two possible answers to the question: Who is a robot?

Who is a robot? A fundamental model of artificial identity

This study investigates how the level of physical anthropomorphism in conversational agents affects their persuasiveness. The experiment compared robots with low and high levels of anthropomorphism, akin to human appearance. Results indicated that while sophisticated physical embodiment did not universally enhance persuasiveness, it did have an interaction effect based on issue involvement. For low involvement issues, persuasiveness increased with higher anthropomorphism. In contrast, for high involvement issues, persuasiveness remained constant across different levels of anthropomorphism but dropped when robots closely resembled humans, possibly due to trust in high-tech robots. The findings highlight the importance of digital agents persuasive power and ethics in employing and designing robots.

The Impact of Physical Anthropomorphism in Social Robots on User Compliance: The Moderating Effect of Issue Involvement

The seamless integration and acceptance of humanoid service robots in society requires a general understanding of those robots. An overall transparent design can be a means to establish this. To guide the implementation of effective transparency mechanisms, we propose the novel Transparency Design Model, which builds on the concept of Interaction Transparency from our previous work. This model encompasses a wide range of transparency aspects aimed to improve the overall transparency of humanoid service robot designs. To demonstrate the applicability of our model and gather initial insights on its impact on robot perception, we conducted a user study involving 50 participants. The study compared a neutral and transparent robot behavior within a complex sales service scenario. Our findings indicate that the transparent design significantly improve the robots likeability, anthropomorphism, and benevolence. Additionally, we observed positive trends in attractiveness, stimulation, and novelty as measured by the User Experience Questionnaire. These measurable effects highlight the important role of Interaction Transparency. The results also imply that a holistic system analysis in multiple complex and application-dependent interactions is worthwhile as the service content influences the perception and effects of the individual design steps. Further, there is a need to develop a new scale for measuring Interaction Transparency. Lastly, future research could benefit from employing multi-modal assessment tools.

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Experimental Evaluation of ROS-Causal in Real-World Human-Robot Spatial Interaction Scenarios

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