United States

Advanced by rich perception and precise execution, robots possess immense potential to provide professional and customized rehabilitation exercises for patients with mobility impairments caused by strokes. Autonomous robotic rehabilitation significantly reduces human workloads in the long and tedious rehabilitation process. However, training a rehabilitation robot is challenging due to the data scarcity issue. This challenge arises from privacy concerns (e.g., the risk of leaking private disease and identity information of patients) during clinical data access and usage. Data from various patients and hospitals cannot be shared for adequate robot training, further compromising rehabilitation safety and limiting implementation scopes. To address this challenge, this work developed a novel federated joint learning (FJL) method to jointly train robots across hospitals. FJL also adopted a long short-term memory network (LSTM)-Transformer learning mechanism to effectively explore the complex tempo-spatial relations among patient mobility conditions and robotic rehabilitation motions. To validate FJL&#39;s effectiveness in training a robot network, a clinic-simulation combined experiment was designed. Real rehabilitation exercise data from 200 patients with stroke diseases (upper limb hemiplegia, Parkinson&#39;s syndrome, and back pain syndrome) were adopted. Inversely driven by clinical data, 300,000 robotic rehabilitation guidances were simulated. FJL proved to be effective in joint rehabilitation learning, performing 20% - 30% better than baseline methods.

IEEE RO-MAN 2024

Federated Joint Learning of Robot Networks in Stroke Rehabilitation

robot rehabilitation

federated learning

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The 20th International Conference of the Italian Association for Artificial Intelligence (**AIxIA 2021**) is organized by AIxIA (Associazione Italiana per l’Intelligenza Artificiale), which is a non-profit scientific society founded in 1988 and devoted to the promotion of Artificial Intelligence. The society aims to increase the public awareness of AI, encourage the teaching of it and promote research in the field.

 
 



**General Chair**

Stefania Bandini, University of Milano-Bicocca, Italyand RCAST - The University of Tokyo, Japan


**Program Chairs**

Viviana Mascardi, University of Genova, Italy; Matteo Palmonari, University of Milano-Bicocca, Italy; Giuseppe Vizzari, University of Milano-Bicocca, Italy

**Organizing Committee**

Francesca Gasparini, University of Milano-Bicocca, Italy (**Chair**); Stefano Ferilli, University of Bari, Italy; Marta Giltri, University of Milano-Bicocca, Italy; Daniela Briola, University of Insubria, Italy


**Publicity and Communication Committee**

Elisabetta Fersini, University of Milano-Bicocca, Italy (**Chair**); Angelo Ferrando, University of Genova, Italy; Francesca Alessandra Lisi, University of Bari, Italy; Giovanni Stilo, University of L'Aquila, Italy; Marco Viviani, University of Milano-Bicocca, Italy


**Workshop Chairs**

Gabriella Cortellessa, ISTC-CNR, Rome, Italy; Silvia Rossi, University of Naples Federico II, Italy; Fabio Stella, University of Milano-Bicocca, Italy


**PC Members**

Fulvio Mastrogiovanni, University of Genova, Italy; Roberto Basili, University of Rome Tor Vergata, Italy; Nicola Gatti, Polytechnic University of Milan, Italy; Stefano Bistarelli, University of Perugia, Italy; Piero Poccianti, Consorzio Operativo Gruppo MPS, Italy; Francesco Scarcello, University of Calabria, Italy; Flavio S. Correa Da Silva, University of São Paulo, Brazil; Elena Cabrio, Université Côte d'Azur, CNRS, Inria, I3S, France; Antonio Lieto, University of Turin, Italy; Diego Calvanese, Free University of Bozen-Bolzano, Italy; Alessio Micheli, University of Pisa, Italy; Francesca A. Lisi University of Bari Aldo Moro, Italy; Mauro Vallati, University of Huddersfield, United Kingdom; Bernardo Magnini, Fondazione Bruno Kessler (FBK), Italy; Luigia C. Aiello, Sapienza University of Rome, Italy; Rafael H. Bordini, Pontifical Catholic University of Rio Grande do Sul, Brazil; Chiara Ghidini, Fondazione Bruno Kessler (FBK), Italy; Federico Bergenti, University of Parma, Italy; Francesco Ricca, University of Calabria, Italy; Andrea Burattin, Technical University of Denmark, Denmark; Angelo Oddi, ISTC-CNR, Italian National Research Council, Italy; Farshad Badie, Aalborg University, Denmark; Emanuel Sallinger, University of Oxford, United Kingdom; Marin Lujak, University Rey Juan Carlos, Spain; Giancarlo Fortino, University of Calabria, Italy; Salvatore Gaglio, University of Palermo, Italy; Johannes K. Fichte, TU Dresden, Germany; Matteo Baldoni, University of Turin, Italy; Fabrizio Riguzzi, Univeristy of Ferrara, Italy; Ivana Dusparic, Trinity College Dublin, Ireland; Serena Villata, CNRS - Laboratoire d'Informatique, Signaux et Systèmes de Sophia-Antipolis, France; Paolo Rosso, Technical University of Valencia, Spain; Ana L. C. Bazzan, Federal University of Rio Grande do Sul, Brazil; Gianluigi Greco, University of Calabria, Italy; Stefano Cagnoni, University of Parma, Italy; Gian Luca Pozzato, University of Turin, Italy; Salvatore Ruggieri, University of Turin, Italy; Claudio Di Ciccio, Sapienza University of Rome, Italy; Eloisa Vargiu, Cetaqua, Water Technology Center, Spain; Marco Maratea, University of Genova, Italy; Andrea Roli, University of Bologna, Italy; Viviana Patti, University of Turin, Italy; Stefania Costantini, University of L'Aquila, Italy; Antonius Weinzierl, Vienna University of Technology, Austria; Silvia Rossi, University of Naples Federico II, Italy; Giovanni Semeraro, University of Bari, Italy; Luciano Serafini, Fondazione Bruno Kessler (FBK), Italy; Laura Giordano, University of Eastern Piedmont, Italy; Ernesto Jimenez-Ruiz, City, University of London, United Kingdom; Marco Gavanelli, University of Ferrara, Italy; Federico Chesani, University of Bologna, Italy

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AIxIA 2021

The 20th International Conference of the Italian Association for Artificial Intelligence (AIxIA 2021) is organized by AIxIA (Associazione Italiana per l’Intelligenza Artificiale), which is a non-profit scientific society founded in 1988 and devoted to the promotion of Artificial Intelligence. The society aims to increase the public awareness of AI, encourage the teaching of it and promote research in the field.

Advanced by rich perception and precise execution, robots possess immense potential to provide professional and customized rehabilitation exercises for patients with mobility impairments caused by strokes. Autonomous robotic rehabilitation significantly reduces human workloads in the long and tedious rehabilitation process. However, training a rehabilitation robot is challenging due to the data scarcity issue. This challenge arises from privacy concerns (e.g., the risk of leaking private disease and identity information of patients) during clinical data access and usage. Data from various patients and hospitals cannot be shared for adequate robot training, further compromising rehabilitation safety and limiting implementation scopes. To address this challenge, this work developed a novel federated joint learning (FJL) method to jointly train robots across hospitals. FJL also adopted a long short-term memory network (LSTM)-Transformer learning mechanism to effectively explore the complex tempo-spatial relations among patient mobility conditions and robotic rehabilitation motions. To validate FJL's effectiveness in training a robot network, a clinic-simulation combined experiment was designed. Real rehabilitation exercise data from 200 patients with stroke diseases (upper limb hemiplegia, Parkinson's syndrome, and back pain syndrome) were adopted. Inversely driven by clinical data, 300,000 robotic rehabilitation guidances were simulated. FJL proved to be effective in joint rehabilitation learning, performing 20% - 30% better than baseline methods.

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”.

Robots that make use of multi-fingered or fully anthropomorphic end-effectors can engage in highly complex manipulation tasks. However, the choice of a suitable grasp for manipulating an object is strongly influenced by factors such as the physical properties of an object and the intended task. This makes predicting an appropriate grasping pose for carrying out a concrete task notably challenging. At the same time, current grasp type predictors rarely consider the task as a part of the prediction process. This work proposes a learning model that considers the task in addition to an object's visual features for predicting a suitable grasp type. Furthermore, we generate a synthetic dataset by simulating robotic grasps on 3D object models based on the BarrettHand end-effector. With an angular similarity of 0.9 and above, our model achieves competitive prediction results compared to grasp type predictors that do not consider the intended task for learning grasps. Finally, to foster research in the field, we make our synthesized dataset available to the research community.

Incorporation of the Intended Task into a Vision-Based Grasp Type Predictor for Multi-Fingered Robotic Grasping

It is crucial that users are empowered to use the functionalities of a robot to creatively solve problems on the fly. A user who has access to a Reinforcement Learning (RL) based robot may want to use the robot's autonomy and their knowledge of its behavior to complete new tasks. One technique is for the user to take control of some of the robot's action space through teleoperation, allowing the RL policy to simultaneously control the rest. We formalize this type of shared control as Partitioned Control (PC). However, this may not be possible using an out-of-the-box RL policy. For example, a user's control may bring the robot into a failure state from the policy's perspective, causing it to act unexpectedly and hindering the success of the user's desired task. In this work, we formalize this problem and present Imaginary Out-of-Distribution Actions, IODA, an initial algorithm which empowers users to leverage their expectations of a robot's behavior to accomplish new tasks. We deploy IODA in a user study with a real robot and find that IODA leads to both better task performance and a higher degree of alignment between robot behavior and user expectation. We also show that in PC, there is a strong and significant correlation between task performance and the robot's ability to meet user expectations, highlighting the need for approaches like IODA. Code is available at https://github.com/AABL-Lab/ioda_roman_2024

Imagining In-Distribution States: How Predictable Robot Behavior Can Enable User Control Over Learned Policies

Augmented reality multi-user interfaces facilitate communication, coordination and collaboration among teams. Moreover, these interfaces can help to align the team's perceptions and expectations under a shared mental model. This model is a psychological construct that represents the common knowledge, beliefs, and understandings held by team members. In this paper, we study to what extent, if any, the combination of Augmented Reality multi-user interfaces and shared mental models affects human-robot trust. To this end, we developed an Augmented Reality multi-user interface to perform a user study ($N=37$) comparing non-dyadic human-robot interactions with a quadruped robot exhibiting low reliability (Group 3), against dyadic interactions while the robot exhibited high-reliability (Group 1) or low-reliability (Group 2). We made this comparison using validated trust questionnaires relevant to HRI. Our results, obtained via Bayesian data analysis methods, show differences in the distribution of answers between groups 1 and 2. Notably, this difference is smaller between groups 1 and 3, which suggests that the combination of shared mental models and multi-user interfaces holds promise as an effective way to manage and calibrate human-robot trust.

On the Effect of Augmented-Reality Multi-User Interfaces and Shared Mental Models on Human-Robot Trust

The sources of a person's ikigai---their sense of meaning and purpose in life---often change as they age. Reflecting on past and new sources of ikigai may help people renew their sense of meaning as their life circumstances shift. Building on insights from an initial Wizard-of-Oz robot prototype, we describe the design of an autonomous robot that uses a semi-structured conversation format to help older adults reflect on what gives their life meaning and purpose. The robot uses both pre-determined (scripted) and Large Language Model (LLM) generated questions to personalize conversations with older adults around themes of social interaction, planning, accomplishments, goal setting, and the recent past. We evaluated the autonomous robot with 19 older adult participants in a lab setting and at two eldercare facilities. Analysis of the older adults' conversations with the robot and their responses to an evaluative survey allowed us to identify several design considerations for an autonomous robot that can support ikigai reflection. Interweaving simple yet detailed pre-determined questions with LLM-generated follow-up questions yielded enjoyable, in-depth conversations with older adults. We also recognized the need for the robot to be able to offer relevant suggestions when participants cannot recall events and people they find meaningful. These findings aim to further refine the design of an interactive robot that can support users in their exploration of life's purpose.

Let's Talk About You: Development and Evaluation of an Autonomous Robot to Support Ikigai Reflection in Older Adults

To facilitate easy, seamless, and dynamic interactions between humans and social robots, it is important that the robots physical appearance gives clear cues to its affordances. However, little is known about what underlying concepts and assumptions shape laypeoples perception and understanding of different robotic embodiments. To explore what robot-inexperienced users expect robots in different application domains to look like, we drew on Research through Design principles to pilot a tangible design kit with which laypeople could prototype robot designs. 27 participants with no background in robotics were asked to design robots for four different application domains. These participants were then further interrogated about their design choices in structured qualitative interviews. The resulting designs primarily ranged from mechanical to humanoid in appearance, though several animal-like entertainment robots were also created. The inductive thematic analysis of participant interviews revealed complex opinions on anthropomorphism in robotic designs and stressed social robots' potential for customization and accessibility. Our findings provide qualitative insight into the beliefs that underlie laypeoples understanding of robotic design in different contexts. Furthermore, we piloted a design toolkit that allows laypeople at any level of creative ability to design and critically reflect on robotic concepts and embodiments.

Human, Animal, or Machine? A Design-Based Exploration of Social Robot Embodiment with a Creative Toolkit

How a robot moves is among the first things an observer notices when they encounter a robot. While considerable research has investigated the perception of robot body language, no studies yet, to our knowledge, have explored the social effects of how a robot moves through space (its gait) on people's first impressions of a robot. To this end, here we performed two complementary experiments online ($n=98$) and in-person ($n=26$), with the objective of determining the extent to which a quadruped robot's gait influences a) what animal people perceived it to be; and b) its social attributes in terms of warmth, competence, causing discomfort and zoomorphism. Results differed depending on whether participation was online or in-person, with gaits influencing participants' perception more markedly when they encountered the robot in-person. Online, most participants saw the robot as a dog for every gait except one, while in-person participants reported more varied responses. Participants in both studies rated the more active, &quot;bouncy&quot; gait as warmer and less discomforting. In-person participants also consistently rated all gaits as warmer, more competent, less discomforting and more zoomorphic than did online participants. The study supports findings that in-person exposure and embodiment affect a robot's social perception and further suggests that gait may have a limited effect as well.

Walking the Line: Assessing the Role of Gait in a Quadruped Robot's Perception

The need for robots to explain their failures grows as the variety and number of robots deployed in public, homes, and work environments increases. This paper extends our prior work utilizing explanation templates by comparing those Templated explanations to Generative explanations created by a Large Language Model. Our study surprisingly reveals that Templated explanations result in similar or higher perceived intelligence and trust while also being more understandable. Through our findings, we aim to provide considerations for effective robot explanation systems, ultimately enabling people to be able to understand and provide assistance to robots that have encountered unforeseen circumstances.

Templated vs. Generative: Explaining Robot Failures

The use of dialogue agents with a strategy that focuses beyond a static depth level and gradually deepens the conversation over time can nurture relationships with users. Thus, this study aimed to develop a dialogue agent capable of gradually deepening conversations over time and consequently examine its effectiveness. First, we constructed a topic depth estimation model capable of estimating the depth of any given topic. Subsequently, we developed a dialogue agent designed to gradually deepen conversation topics as interactions progress, tailored for operations on users' smartphones. To evaluate the effectiveness of the developed agent, we conducted a 10-day dialogue experiment with 26 participants. The results indicated that the proposed agent elicited deep self-disclosure from users without significantly increasing psychological reluctance compared to the control condition. Moreover, the participants perceived a deeper relationship with the agent and were less likely to consider it as a non-human entity. These findings suggest that the proposed dialogue agent was capable of effectively engaging in deeper conversations over time, thereby enhancing human-agent interactions.

Deepening Conversations Over Time: A Chatbot with a Topic Depth Estimation Model for Gradually Engaging in Deeper Chats

This work introduces a robotics platform which comprehensively integrates multi-step action planning, natural language understanding, and memory to interactively perform service tasks in accordance with variable needs and intentions of users. The proposed architecture is built around an AI agent, derived from GPT-4, which has been embedded in an embodied system. Our approach utilizes semantic matching, plan validation, and state messages to ground the agent in the physical world, enabling a seamless merger between communication and behavior. We demonstrated the advantages of this system with an HRI study comparing mobile robots with and without conversational AI capabilities in a free-form tour-guide scenario. The increased adaptability of the system is measured along five dimensions: flexible tasking planning, interactive exploration of information, emotional friendliness, personalization, and increased overall user satisfaction.

Dobby: A Conversational Service Robot Driven by GPT-4

For robots to effectively collaborate with human partners, they need to be able to understand what instructions and/or statements mean in the context of their environment. To ground objects in a dynamic world, robots need an understanding of how both spatial and temporal relationships evolve as a function of time in the environment. However, it is computationally intensive to classify, track, and predict the motion of all objects. Approaches based on Language-Guided Temporally Adaptive Perception (LGTAP) utilize information embedded in the instruction to selectively classify objects to construct minimal but sufficiently detailed models of the environment for symbol grounding. Such methods, however, fail when the instruction refers to the future state of the environment as it lacks any notion of whether to and for how long a future prediction is necessary to ground the instruction. This prompts a reformulation of LGTAP that can selectively utilize information from past observations to accurately predict the future state of objects. This paper describes a novel approach for LGTAP for instructions that may refer to the past, present, and/or future state of the environment by closing the loop around symbol grounding and adaptive perception. A detailed analysis of a grounding problem that refers to the future state of the environment, a corpus-based analysis of performance, and a physical demonstration of natural language understanding is presented along with a description of this novel architecture.

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Incorporation of the Intended Task into a Vision-Based Grasp Type Predictor for Multi-Fingered Robotic Grasping

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