United States

Spoken language interaction is at the heart of interpersonal communication, and people flexibly adapt their speech to different individuals and environments. It is surprising that robots, and by extension other digital devices, are not equipped to adapt their speech and instead rely on fixed speech parameters, which often hinder comprehension by the user. We conducted a speech comprehension study involving 39 participants who were exposed to different environmental and contextual conditions. During the experiment, the robot articulated words using different vocal parameters, and the participants were tasked with both recognising the spoken words and rating their subjective impression of the robot&#39;s speech. The experiment&#39;s primary outcome shows that spaces with good acoustic quality positively correlate with intelligibility and user experience. However, increasing the distance between the user and the robot exacerbated the user experience, while distracting background sounds significantly reduced speech recognition accuracy and user satisfaction. We next built an adaptive voice for the robot. For this, the robot needs to know how difficult it is for a user to understand spoken language in a particular setting. We present a prediction model that rates how annoying the ambient acoustic environment is and, consequentially, how hard it is to understand someone in this setting. Then, we develop a convolutional neural network model to adapt the robot&#39;s speech parameters to different users and spaces, while taking into account the influence of ambient acoustics on intelligibility. Finally, we present an evaluation with 27 users, demonstrating superior intelligibility and user experience with adaptive voice parameters compared to fixed voice.

IEEE RO-MAN 2024

No More Mumbles: Enhancing Robot Intelligibility through Speech Adaptation

human-centered robotics

robot voice communication

adaptive speech

[![](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!

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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”.

Spoken language interaction is at the heart of interpersonal communication, and people flexibly adapt their speech to different individuals and environments. It is surprising that robots, and by extension other digital devices, are not equipped to adapt their speech and instead rely on fixed speech parameters, which often hinder comprehension by the user. We conducted a speech comprehension study involving 39 participants who were exposed to different environmental and contextual conditions. During the experiment, the robot articulated words using different vocal parameters, and the participants were tasked with both recognising the spoken words and rating their subjective impression of the robot's speech. The experiment's primary outcome shows that spaces with good acoustic quality positively correlate with intelligibility and user experience. However, increasing the distance between the user and the robot exacerbated the user experience, while distracting background sounds significantly reduced speech recognition accuracy and user satisfaction. We next built an adaptive voice for the robot. For this, the robot needs to know how difficult it is for a user to understand spoken language in a particular setting. We present a prediction model that rates how annoying the ambient acoustic environment is and, consequentially, how hard it is to understand someone in this setting. Then, we develop a convolutional neural network model to adapt the robot's speech parameters to different users and spaces, while taking into account the influence of ambient acoustics on intelligibility. Finally, we present an evaluation with 27 users, demonstrating superior intelligibility and user experience with adaptive voice parameters compared to fixed voice.

technical paper

Following an accident or motor impairment from illness, individuals often experience reduced mobility. When lower limb function is affected, individuals may lose the ability to walk and rely on a wheelchair. Despite maintaining upper limb mobility, emotional and societal factors may reduce motivation to use them. The Volting project aims to address this by developing a wheelchair with improved mobility to help users achieve their maximum potential. To overcome the limitations of traditional wheelchair controls, a device called WISP was developed, allowing control of Volting through trunk movements. This not only frees the upper limbs but also encourages users to engage their bodies in mobility. Initial experiments with a professional wheelchair dancer showed rapid adaptation to WISP usage. However, gradual implementation is recommended for non-athletic wheelchair users due to the risk of injury from unfamiliar movements. Therefore, we present a training system for WISP utilization, combining a virtual environment with haptic feedback to aid learning and adjust difficulty levels. Preliminary results demonstrate significant improvements in task completion time, collisions, and spatial errors after three sessions in our virtual environment. Additionally, trunk movements decreased, indicating more efficient adaptation to WISP use. As the next step, we plan to conduct trials with individuals with disabilities in our virtual training system, followed by integrating WISP with Volting to explore enhanced mobility.

Enhancing Wheelchair Mobility: Virtual Training System Integrating Lateral Trunk Motion

Social robots are rarely confined to a task-specific or problem-specific work envelope. For instance, an assistive robot working in a senior center can incidentally become a companion or even a trusted confidante. Increasingly operating in public spaces, social robots can be easily caught in mundane yet morally ambivalent urban situations that are often dynamic, complex, and unpredictable, presenting novel factors and agencies that can quickly exceed the scope of any projected human-robot interaction. Anticipating how a robot should respond in these situations may mitigate interaction failures. To improve foresight in these situations, we propose the frame of Situated Ethics for Urban Encounters (SEUE) and probe this frame through 18 mundane yet morally ambivalent scenarios of human-robot interaction in a public library using the speed-dating research method with storyboards. Our findings suggest: (i) There are ambiguous but morally important interactions even in a public library; (ii) Unlike humans, robots are expected to do many things at once and some actions may risk deception; (iii) The importance of childrens rights in these situations; (iv) A need for creatively opportunistic interaction when responding to the ethical challenges of the situation; and (v) Residual risks exist even in a perceived ethical response on the part of the robot. We contribute to empirical HRI ethics by demonstrating how the speed-dating technique in a participatory design setting could be used to unpack, interpret, discuss, and elicit possible HRI responses in urban situations, which are expected to only become more important as robots show up in cities.

Robots among Us: An Exploratory Study on the Situated Ethics of Human-Robot Interactions in a Public Library

In recent years, the awareness of the academy around responsible research has notably increased. For instance, with advances in machine learning and artificial intelligence, recent efforts have been made to promote ethical, fair, and inclusive AI and robotics. However, the field of human-robot-interaction (HRI) is seemingly lagging behind these practices. To better understand if and to what extent HRI is incentivizing researchers to engage in responsible research, we conducted an exploratory review of the publishing guidelines for the most popular HRI conference venues. We identified 18 conferences which published at least 7 HRI papers in 2022. From these, we discuss four themes relevant to conducting responsible HRI research in line with the Responsible Research and Innovation framework: ethical and human participant considerations, transparency and reproducibility, accessibility and inclusion, and plagiarism and LLM use. We identify several gaps and room for improvement within HRI regarding responsible research. Finally, we establish a call to action to provoke novel conversations among HRI researchers about the importance of conducting responsible research within emerging fields like HRI.

HRI Wasn't Built in a Day: A Call to Action for Responsible HRI Research

Industrial robot programming necessitates specialized expertise and significant time commitment, particularly for small-batch productions. In response to the escalating demand for production agility, novel approaches have emerged in intuitive robot programming. These inventive systems, rooted in diverse conceptual frameworks, are designed to expedite the deployment of robot systems. A prominent innovation in this domain is adopting no-code robot programming through finger-based gestures. A robot program can be generated by capturing and tracking non-expert users' finger movements and gestures, converting 3D coordinates into an executable robot programming language. However, accurately determining finger positions for 3D coordinates and precise geometrical features presents an ongoing challenge. In pursuit of heightened trajectory precision and reducing more significant effort for the users, we propose a hybrid methodology that amalgamates finger-gesture programming with point cloud data. This synergistic integration demonstrates promising outcomes, substantiating its potential to facilitate the precise and adaptive generation of robot paths within real industrial scenarios.

A Hybrid Approach of No-Code Robot Programming for Agile Production: Integrating Finger-Gesture and Point Cloud

This paper presents a novel approach to multi-task visual-guided imitation learning. Upon evaluating the current state-of-the-art method, we observed its capability to replicate the intent of the demonstrator, but with the flaw of manipulating incorrect objects. To address this issue, our study introduces a new approach based on the assumption that explicitly addressing task-relevant problems, such as target object localization, can enhance system performance. Our validation shows that the proposal overtakes the leading method thanks to its ability to drive the robot motion towards the target object.

Improving Learning from Visual Demonstration Methods by Target Localization

In this paper, we build upon two major recent developments in the field, Diffusion Policies for visuomotor manipulation and large pre-trained multimodal foundational models to obtain a robotic skill learning system. The system can obtain new skills via the behavioral cloning approach of visuomotor diffusion policies given teleoperated demonstrations. Foundational models are being used to perform skill selection given the user's prompt in natural language. Before executing a skill the foundational model performs a precondition check given an observation of the workspace. We compare the performance of different foundational models to this end and give a detailed experimental evaluation of the skills taught by the user in simulation and the real world. Finally, we showcase the combined system on a challenging food serving scenario in the real world. Videos of all experimental executions, as well as the process of teaching new skills in simulation and the real world, are available on the project's website.

A Robotic Skill Learning System Built Upon Diffusion Policies and Foundation Models

For social robots to succeed in places such as homes, they must learn new skills from various people and act in a manner desirable to different users. We introduce a novel biologically inspired approach for robot learning through program-level imitation, inspired by the way primates, including humans, understand and perform complex actions. Our approach enables robots to discover the hierarchical structure of tasks by identifying sequential regularities and sub-goals from diverse human demonstrations. To do so, human-provided demonstrations, which can be obtained by a robot through different modalities (such as kinesthetic teaching, behavioural observation, and verbal instruction), are processed by an algorithm that discovers multiple possibilities for arranging observed sub-goals to achieve a final goal. Prior to acting, the available sequences are evaluated based on user-defined criteria, through mental simulation of the task by the robot, to find the optimal sequence of actions. As a proof-of-concept, we implemented our system on an iCub humanoid robot and present here how our method allowed the robot to adapt its action sequences for task execution when starting the task from different states, incorporating user preference for finishing the task as fast as possible. Our envisaged system is meant to accommodate variations in human teaching styles and is expected to help a robot perform tasks with greater flexibility and efficiency. This work contributes by proposing a framework for robots to learn from humans at an abstract level, opening the way to more adaptable and intelligent robotic assistants in everyday tasks.

A Biologically Inspired Program-Level Imitation Approach for Robots: Proof-Of-Concept

Dialogs between humans and social robots are often fact-oriented and not very mood-dependent, at least on the part of the robot. This raises the question of what influence the tonality of the utterances has on the perceived personality of the robot and how this influences the user's further expectations. One element that is not necessarily expected from robots in conversation is humor, especially the use of irony and sarcasm. We examined the effect of a conversation with cheerfully ironic or sullenly sarcastic remarks by the robot in contrast to utterances in a more seriously neutral tone. To measure these effects in terms of perceived warmth, competence and possible discomfort in conversation we used the Robotic Social Attributes Scale (RoSAS) on 20 participants with an age ranging from 26 to 58. In addition, we asked the participants for their assessment of the suitability of the robot's dialog style for different areas of application and which dialog style they personally liked best. The dialog parts of the robot were generated with ChatGPT using suitable prompts. Our results showed that the perceived warmth gained highest rates when when the robot hat a cheerfully ironic tone. Discomfort was rated significantly higher when a sullenly sarcastic tone was used by the robot. Perceived competence seemed to be slightly negatively influenced in the sarcastic condition. The results proved that tonality appears to be a very relevant design element for a successful interaction between humans and robots, but must be used carefully to achieve the desired goal.

Introducing a Note of Levity to Human-Robot Interaction with Dialogs Containing Irony, Sarcasm and Jocularity

The goal-directed behavior observed in humans arises from the intricate interplay of various processes, including personality dynamics, emotional responses to others, memory encoding, the anticipation of future actions, and associated hedonic experiences. Integrating these multiple processes characteristic of human intelligence into a robotic framework aims to enhance the human-likeness of artificial agents and facilitate more natural and intuitive interactions with humans.   For this purpose, in this paper, we propose a comprehensive psychological and cognitive architecture where, personality, as it happens for humans, not only influences the execution of actions but also shapes internal reactions to human emotions and guides anticipatory decision-making processes tailored to the agent's traits. We demonstrate the framework's effectiveness in generating perceivable synthetic personalities through an experiment involving participants in a dyadic conversation scenario with a digital human, where the digital human's behavior is driven by its assigned personality. The results show that participants accurately perceive the artificial personality displayed by the digital human. We also demonstrate the potential of our robotic framework to bridge the gap between cognitive and psychological agents, as the findings highlight its ability to create a cognitively and emotionally intelligent digital human.

Personality and Memory-Based Framework for Emotionally Intelligent Agents

To enable sophisticated interactions between humans and robots in a shared environment, robots must infer the intentions and strategies of their human counterparts. This inference can provide a competitive edge to the robot or enhance human-robot collaboration by reducing the necessity for explicit communication about task decisions. In this work, we identify specific states within the shared environment, which we refer to as Critical Decision Points, where the actions of a human would be especially indicative of their high-level strategy. A robot can significantly reduce uncertainty regarding the human's strategy by observing actions at these points. To demonstrate the practical value of Critical Decision Points, we propose a Receding Horizon Planning (RHP) approach for the robot to influence the movement of a human opponent in a competitive game of hide-and-seek in a partially observable setting. The human plays as the hider and the robot plays as the seeker. We show that the seeker can influence the hider to move towards Critical Decision Points, and this can facilitate a more accurate estimation of the hider's strategy. In turn, this helps the seeker catch the hider faster than estimating the hider's strategy whenever the hider is visible or when the seeker only optimizes for minimizing its distance to the hider.

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Enhancing Wheelchair Mobility: Virtual Training System Integrating Lateral Trunk Motion

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