United States

This paper demonstrates the effectiveness of learning based models for accurate, and reliable robot to human handoffs in various HRI scenarios. Specifically we bench marked a neuromorphic spiking neural network and a time series k-nearest neighbors classifier against traditional hand crafted force threshold methods. These models use linear force in the x, y, and z direction, as well as torque about the x, y, and z axis at the end effector of the robot arm to make handoff predictions. This paper demonstrates that these learning based methods are more robust to noise which occurs during operational use. We applied our algorithms to both stationary handoffs (stationary robot) and moving handoffs (robot walking). We believe that our evaluation is the first to examine walking handoffs. We evaluated all models in tests which determined the accuracy, precision, recall, f1, and average execution time for handoff events, noise events, and no event tests. We find that the SLAYER spiking neural network model performed the best across both walking and stationary handoffs for the majority of the evaluation criteria. Our results suggest that neuromorphic spiking neural networks are strong contenders for applications in time series, event based HRI applications.

IEEE RO-MAN 2024

Spiking Neural Networks for Improved Robot-Human Handoffs

cooperation and collaboration in human-robot teams

detecting and understanding human activity

neuromorphic computing

### Welcome to the 8th annual workshop on Naval Applications of Machine Learning!

NAML is the premier annual event showcasing current machine learning research relevant to Naval applications. We welcome participation from the defense community, industry, and academia. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

**General Chairs:**

Katie Rainey, Naval Information Warfare Center Pacific

Nicole Isoda, Naval Information Warfare Center Pacific 

**[More about NAML 2024](https://sites.google.com/go.spawar.navy.mil/naml/)**

Please log in to explore this event. You need to log in with the email address you registered with. Access credentials have been sent to your email. 

Please be sure to check your spam and other email folders if you do not see an email confirmation right away.

Please log in to explore this workshop.

Registration for this workshop has now closed. We appreciate your interest and hope to welcome you at a future event. If you’d like to hear about similar upcoming events, please check our website or join our mailing list.

Registration for this workshop has now closed.

NAML 2024

The 8th annual workshop on Naval Applications of Machine Learning is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. The goals of NAML are to spread awareness of current machine learning research relevant to Naval applications, to connect machine learning researchers with experts in military requirements and to build and strengthen collaborations within the defense research community.


technical paper

[![](https://assets.underline.io/markdown_image/1/image/f25361a85b7c83a7476c0b3c1c0bb1b2.png)](https://drive.google.com/file/d/18jcAcciCBQHj6R07Dbpm2HCOBEIwAOCU/view?usp=sharing)


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!

We hope you continue to enjoy the post-conference content on Underline!

You need to log in with the email address you registered with. Access credentials have been sent to your email. 

Please be sure to check your spam and other email folders if you do not see an email confirmation right away.

Please log in to explore this event.

It looks like you are not registered for this event. 

A registration is required to access this event page. 



Registration Is Required

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”.

Using a dual-task paradigm, we explore how robot actions, performance, and the introduction of a secondary task influence human trust and engagement. In our study, a human supervisor simultaneously engages in a target-tracking task while supervising a mobile manipulator performing an object collection task. The robot can either autonomously collect the object or ask for human assistance. The human supervisor also has the choice to rely on or interrupt the robot. Using data from initial experiments, we model the dynamics of human trust and engagement using a linear dynamical system (LDS). Furthermore, we develop a human action model to define the probability of human reliance on the robot. Our model suggests that participants are more likely to interrupt the robot when their trust and engagement are low during high-complexity collection tasks. Using Model Predictive Control (MPC), we design an optimal assistance-seeking policy. Evaluation experiments demonstrate the superior performance of the MPC policy over the baseline policy for most participants.

Assistance-Seeking in Human-Supervised Autonomy: Role of Trust and Secondary Task Engagement

While robots have advanced in understanding their environments, collaborative tasks demand a deeper comprehension of human intentions to mitigate uncertainty. Anticipatory and proactive behaviours are pivotal in enhancing Human-Robot Interactions (HRI), yet literature often conflates these terms. This study elucidates the distinction between anticipation and proactivity, offering clear definitions and exemplifying their implications through a handover scenario. Through a user study with 24 volunteers performing a total of 72 experiments, we have found that humans are able to distinguish both behaviours and that there is a statistically significant increase in the anthropomorphism of the robot when it behaves proactively. Additionally, both anticipation and proactivity show statistically significant increases in multiple aspects of effective HRI (fluency, comfort, performance, etc.). However, no clear preference for either has been detected.

Anticipation and Proactivity. Unraveling Both Concepts in Human-Robot Interaction through a Handover Example

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.

Evaluating Leadership Roles in Human-Robot Interaction Via Highly Dynamic Collaborative Tasks

This paper investigates how conversational agents that are embedded in smart environments should present themselves socially. In an online study, we simulate a future space habitat in which astronauts (participants) interact with one or more agents to complete several tasks related to science, maintenance, and inventory. We examine effects of agent affiliation (affiliation with a user, affiliation with a domain, or affiliation with all users and all domains) and narrative perspective (first- vs. third-person references to parts of the environment) on mental models of the smart environment as one or multiple entities, trust, performance, and social variables. Our findings suggest that in this type of setting, interacting with a single agent may increase mental demand, and that agents that speak about embodied interaction in third person are perceived as more trustworthy and competent than agents that speak in first person.

Contrasting Affiliation and Reference Cues for Conversational Agents in Smart Environments

This study explored how a table-top robot as a recipient of explanations can be applied to the education of students with autism spectrum disorder and intellectual disability. Four 17-year-old participants were recruited from a special needs school. We observed their engagement in assigned tasks and any significant interactions with the robot during the three personalized learning sessions in the school environment. During sessions without a robot, each student was asked to solve arithmetic questions related to purchasing items. During sessions with a robot, each student was required to not only solve questions but also explain the processes or results of simple calculations to the robot. We examined the behavioral, affective, and cognitive engagement of each participant during personalized learning sessions. The results showed that participants solved problems faster, more accurately, or with fewer distractions in the with-robot conditions compared to the task-only conditions. It suggests that this approach, where students explain what they know to a robot, might be effectively adopted for students in special needs schools. Future research should examine the role of robots more comprehensively, acknowledging the limitations of this study, such as the measurement of only three sessions.

Students with Autism Spectrum Disorder and Intellectual Disability Teach a Robot

The landscape of education with social robots is evolving, especially within the realm of music education. However, past studies have focused on children as music learners and such verbal behaviors as praise. Therefore, it remains unknown whether existing studies are effective for adult learners in musical education as well as how to effectively design the non-verbal behaviors of robots. This study investigates the effective behaviors of social robots by comparing three kinds of listening behaviors: none, nodding (simple listening), and enjoying (affective listening). We developed a music education support system that consists of a social robot and a MIDI keyboard and conducted an experiment with adult participants. Our experimental results described the advantages of affective-listening behavior during music education over simple listening and non-listening based on gender.

Effects of Listening Behaviors of a Social Robot on Adult's Motivation and Performance in Piano Practice

Stroke-induced aphasia, an acquired speech impairment, poses significant challenges to individuals' communication abilities. Our robot-assisted speech training app aims to facilitate home-based, self-administered training for individuals with aphasia to complement their speech therapy. We evaluated our app in a single-session study with four individuals with aphasia in a rehabilitation setting. Each participant had a different severity grade of the condition, including one case of global aphasia. Our findings suggest an overall positive user experience, with indications that the training facilitated by the robot is suitable for individuals across all severity grades of aphasia, though exercise customization is crucial. Participants, even those with limited technical experience, adapted to using the system by themselves quickly. However, usability issues specific to this diverse target group were noted, such as the length of explanations and the abundant use of robot gestures, which will be addressed in future iterations.

"Repeat after Me" - Exploring Robot-Assisted Speech Training for Varied Aphasia Severities*

Social robots are being introduced in a variety of educational domains with great success. These social robots are often designed by drawing inspiration from practices held by human teachers. Contingent mediations are a prime example of a high-quality teaching practice that supports better outcomes in human-human teaching. This can inform the design of social robots. Current research on robot use in education has focused on curriculum-level contingent mediations where the difficulty level of subsequent tasks are adjusted to the current capabilities of a learner. However, task-level contingent mediations that provide support for students learning during a specific task remain unexplored. This research investigates whether patterns of task-level mediations differ between a robot, virtual, and human agent, as well as their effects on learning outcomes. To investigate these research questions, we designed instruction that utilizes contingent mediational flows, based on formative assessment data, to be delivered by a human, robot, and virtual agent to teach grade 3-5 children science words. We identified 23 unique instructional patterns. Then, we compared these patterns across teaching agents and their effects on learning outcomes. Overall, our study demonstrated that high-quality contingent mediations support childrens science vocabulary in learning regardless of the teaching agent.

Exploring Task-Level Contingent Mediations for Vocabulary Instruction across Robot, Virtual, and Human Teachers

Parental estrangement can cause long-term harm to socio-emotional skills, mental health and wellbeing. This is pronounced for children who experienced trauma. Robo- Therapists have been used to train children to express emotions, but such robots are often human replacements and tend to be too costly to deploy widely. We propose e-MoBo, a low-cost, non-humanoid Robo-Mediator of our own design to train childrens socio-emotional skills towards creating stronger relationships with others. We report on the developing e-MoBo and results of a field study with the current prototype and 14 neurotypical children, ages 5-9, as we prepare to deploy e-MoBo in a residential center with traumatized children. Our results suggest that e-MoBo, compared to a control, helps children express emotions, offering the promise of a low-cost, nonanthropomorphic, Robo-Mediator meeting childrens needs.

E-MoBo, a Low-Cost, “Robo-Mediator” Helping Therapists Teach Children How to Express Emotions Insights from Field Testing

Robots powered by Artificial Intelligence require continuous sensing to function and interact autonomously with their environment. This may create concerns for their users. Here, we describe a study in the context of an office environment equipped with autonomous delivery robots. Our study aims to understand the employees' current comprehension of the data captured by the robots, the nature of their concerns and ways to mitigate those. We found that the employees have little knowledge about data capture and that providing such knowledge can go either way, reassuring or generating new concerns which are often contextual. We qualitatively analysed reasons for having or not having concerns. Our findings include that trust in the employer is an important factor limiting them.

Premium content

Downloads

Next from IEEE RO-MAN 2024

Assistance-Seeking in Human-Supervised Autonomy: Role of Trust and Secondary Task Engagement

Similar lecture

Neuromorphic Robot-Human Handoffs