United States

Vascular dysfunction, trauma, and other disorders often cause a loss in mobility. Brain-computer-aided prosthetics have the potential to help restore critical motor functions and carry out normal daily activities. Through brain-computer interfaces (BCI), electroencephalogram (EEG) brain signals can be used to generate prosthetic gesture commands and control external devices. However, due to the complexity posed by noise in EEG signals, it can be difficult to extract useful features and classify a wide range of motor tasks. In this study, an EEG Motor Movement and Imagery Dataset collected by the New York State Department of Health using a BCI2000 system, is used to perform detailed motor task classification. This dataset includes both real and imagined motions across six different tasks. Convolutional neural networks (CNN) are built to classify six tasks, ultimately leading to binary classification between real and imagined movements, together with motion and rest. The latter models achieve accuracies of 91.2% and 74.4%, respectively. Our approach has the potential for broader applications where comprehensive classifications are needed to discern multiple prosthetic movement tasks.

IEEE RO-MAN 2024

Multi and Binary Task Classification for Real and Imagined Motor Movements Using EEG Signals

eeg signals

classification


IWSSIP is an international conference that brings together researchers and developers from both academia and industry reporting the latest scientific and theoretical advances to discuss and debate major issues and state-of-the-art aspects on systems, signals and image processing.

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IWSSIP 2021

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”.

As robots become more and more integrated in human spaces, it is increasingly important for them to be able to explain their decisions to the people they interact with. These explanations need to be generated automatically and in real-time in response to decisions taken in dynamic and often unstructured environments. However, most research in explainable human-robot interaction only considers explanations (often manually selected) presented in controlled environments. We present an explanation generation method based on counterfactuals and demonstrate its use in an ``in-the-wild&quot; experiment using automatically generated and selected explanations of autonomous interactions with real people to assess the effect of these explanations on participants' ability to predict the robot's behaviour in hypothetical scenarios. Our results suggest that explanations aid one's ability to predict the robot's behaviour, but also that the addition of counterfactual statements may add some burden and counteract this beneficial effect.

What Would I Do If...? Promoting Understanding in HRI through Real-Time Explanations in the Wild

Bio-inspired robotic systems are capable of adaptive learning, scalable control, and efficient information processing. Enabling real-time decision-making for such systems is critical to respond to dynamic changes in the environment. We focus on dynamic target tracking in open areas using a robotic six-degree-of-freedom manipulator with a bird-eye view camera for visual feedback, and by deploying the Neurodynamical Computational Framework (NeuCF). NeuCF is a recently developed bio-inspired model for target tracking based on Dynamic Neural Fields (DNFs) and Stochastic Optimal Control (SOC) theory. It has been trained for reaching actions on a planar surface toward localized visual beacons, and it can re-target or generate stop signals on the fly based on changes in the environment (e.g., a new target has emerged, or an existing one has been removed). We evaluated our system over various target-reaching scenarios. In all experiments, NeuCF had high end-effector positional accuracy, generated smooth trajectories, and provided reduced path lengths compared with a baseline cubic polynomial trajectory generator. In all, the developed system offers a robust and dynamic-aware robotic manipulation approach that affords real-time decision-making.

Adaptive Environment-Aware Robotic Arm Reaching Based on a Bio-Inspired Neurodynamical Computational Framework

With the latest developments in sensors, battery, and Artificial Intelligence (AI) technologies, robots can perform missions in unstructured environments (e.g., disaster sites). However, their adaptation speed is still slow due to their limited onboard computing capability and dynamic disruption from environments, leading to inaccurate planning and delayed emergency reactions. Even though reinforcement learning increases robots' exploration speed by fusing effective guidance, the numerous interactions make the learning time-consuming and even risky (e.g., collisions). To fundamentally improve robot adaptation speed, this work seeks help from quantum power. A novel Quantum Exploration based Dreamer model (QED) was developed to facilitate reinforcement learning explorations. QED based on stochastic quantum walker quickly explores environments, evaluates action quality, and obtains a global optimal exploration strategy; then, these high-quality exploration samples will be used to facilitate reinforcement learning speed. A theoretical benefit of QED is facilitating reinforcement learning speed without changing the underlying learning architecture, which makes the proposed QED applicable to general robot learning scenarios. To validate QED effectiveness, a robot path-planning task in an obstacle-dense environment was designed. The number of needed training episodes validated the effectiveness. The results show that QED achieves around ten times faster policy learning compared with Monte Carlo tree-based reinforcement learning methods and vanilla reinforcement learning methods.

Quantum Exploration-Based Reinforcement Learning for Efficient Robot Path Planning in Sparse-Reward Environment

This paper presents a new software framework for HRI experimentation with the sixth version of the common NAO robot produced by the United Robotics Group. Embracing the common demand of researchers for better performance and new features for NAO, the authors took advantage of the ability to run ROS2 onboard on the NAO to develop a framework independent of the APIs provided by the manufacturer. Such a system provides NAO with not only the basic skills of a humanoid robot such as walking and reproducing movements of interest but also features often used in HRI such as: speech recognition/synthesis, face and object detention, and the use of Generative Pre-trained Transformer (GPT) models for conversation. The developed code is therefore configured as a ready-to-use but also highly expandable and improvable tool thanks to the possibilities provided by the ROS community. The code repository is: https://github.com/antbono/OAN

Open Access NAO (OAN): A ROS2-Based Software Framework for HRI Applications with the NAO Robot

People struggle to form accurate expectations of robots because we typically associate behavior (and capability) with the physical entity even when there are clear indicators of different software programs dictating behavior at different times. This is a harmful prior, as commercially available, visibly similar robots do not necessarily share any common ground in terms of capability, safety, or behavior. Prior efforts to calibrate peoples expectations of robots have not extended to anchoring on the robots control software rather than its embodiment. In this work, we leverage social participation and flexible identity presentation to facilitate coworkers' associations of robot capability with the currently running software rather than physical entity itself. By linking each of a robot's controllers to a social identity, we enable collaborators to more easily differentiate between them. In a human subjects study (n=30), participants who experienced our social identity signal understood differences between the robot's two controllers and prevented an unreliable controller from harming perceptions of the robot's other controller.

Robot Social Identity Performance Facilitates Contextually-Driven Trust Calibration and Accurate Human Assessments of Robot Capabilities

HRI research using autonomous robots in real-world settings can produce results with the highest ecological validity of any study modality, but many difficulties limit such studies feasibility and effectiveness. We propose VID2REAL HRI, a research framework to maximize real-world insights offered by video-based studies. The VID2REAL HRI framework was used to design an online study using first-person videos of robots as real-world encounter surrogates. The online study (n = 385) distinguished the within-subjects effects of four robot behavioral conditions on perceived social intelligence and human willingness to help the robot enter an exterior door. A real-world, between-subjects replication (n = 26) using two conditions confirmed the validity of the online studys findings and the sufficiency of the participant recruitment target (22) based on a power analysis of online study results. The VID2REAL HRI framework offers HRI researchers a principled way to take advantage of the efficiency of video-based study modalities while generating directly transferable knowledge of real-world HRI. Code and data from the study are provided at vid2real.github.io/vid2realHRI.

Vid2RealHRI: Align Video-Based HRI Study Designs with Real-World Settings

In Japan, home robots have become popular and are widely loved. In particular, Sonys aibo users have formed a big community, and a society where humans and robots live in harmony is becoming a reality. In order for robots to be more accepted in the society, exploratory analysis and explicit description of their bonds are important. However, the scale for measuring relationships with robots has not yet been defined, so a suitable measure was created from the Friendship Function Scale to measure the relationship of friends and the Pet Awareness Scale to measure the attitude toward pets. Using the proposed scale, we investigated the role of home robots, subjective intimacy, and attitudes toward robots among 54 robot owners. The survey found that more than half of them considered robots to be pets or children. In addition, the average value of subjective intimacy was very high to indicate a very high level of attachment to the robot. Furthermore, as a result of factor analysis of the scale items, five factors were extracted: Comfort, Irreplaceability, Trust from the robot, Trust to the robot, and Social life. At this time, there was no bias in how users felt about comfort and irreplaceability, but there was a bias in how users felt about trust. Additionally, those with the highest level of subjective intimacy felt significantly more trusted by the robot than those with the other level of intimacy, suggesting that recognition of trust from the robot may be an important bond-forming strategy. In the future, we will aim to form further bonds by investigating relationships with robots that play various roles, as well as in regions other than Japan.

An Exploratory Analysis of the Harmonious Bond between Home Robots and Their Owners in Japan

While extant research on human-robot-interactions (HRI) has dealt with the examination of different human characteristics, quantifying and describing the various char- acteristics of human diversity remains a challenge for HRI research. This in turn often leads to a disregard of human diversity in the design of HRI. Addressing this issue, we conducted a systematic synthesis of existing models on human diversity, culminating in the development of a model we coined the Human Diversity Wheel for Robotic Interactions (HDWRI). The goal of this model is to provide designers and researchers in HRI with an analytical lens to ensure their work considers different human characteristics. To achieve this, we started to conduct expert interviews with HRI researchers to put our model into a practical context. This paper presents the development of our model, preliminary findings of our first interviews, and an outline of future steps.

Towards a Human Diversity Wheel for Human Robot Interaction

To evaluate how explanations affect the users' understanding of robots, researchers typically elicit the user's Mental Model (MM) of the robot and then compare it to the robot's actual decision-making and behaviour. However, the user's self-rating of their level of understanding, which we define as ``user-perceived understanding'', is generally not evaluated. Moreover, this evaluation is typically done only once, while robots are often expected to interact with the same users over long periods. In this work, we suggest a framework to analyse the evolution of the mental models over time across the dimensions of completeness and correctness. We argue that the goal of explainability should be two-fold. On one hand, it should help align the user's perceived understanding with the real one. On the other hand, explainability should enhance the completeness of the mental model to a target level, which varies depending on the user type, while also striving for maximum correctness.

Evaluating the Impact of Explainability on the Users' Mental Models of Robots Over Time

With the growing use of mobile robots in urban settings, their interaction with people in shared spaces is increasing. It's crucial for these robots to navigate in a socially acceptable manner and interact properly with their environment. AI guides robot behavior by interpreting the environment and controlling actions. Poor explainability of robot decisions can lead to mental and physical risks. Proper robot-to-human interfaces can help mitigate these risks. This paper introduces systems and methods for self-explaining mobile robots in common areas using projected information. We analyze data on people's reactions to the robot, with and without self-explainable features, across various university campus settings like maker-spaces, hallways, cafeterias, and study areas. Initial analysis shows improved acceptance of the robot based on task performance and user questionnaires.

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What Would I Do If...? Promoting Understanding in HRI through Real-Time Explanations in the Wild

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