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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Wenshuo Zang Mengsha Hu Rui Liu Cognitive Robotics and AI Lab (CRAI) College of Aeronautics Engineering Department of Computer Science Kent State University OH USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

The rapid advancements of drones have demonstrated the versatility and promising potential of human-swarm systems (HSS) across various domains. However, human performance within these systems may be impaired by factors such as limited domain knowledge and mental stress, often leading to cognitive overload and hindering the efficiency and effectiveness of human-swarm teaming. Consequently, the accurate monitoring of cognitive load levels is crucial for optimizing HSS performance. To address the challenges of existing measurement methods, which are often expensive, time-consuming, or lack real-time capabilities, we propose a Large Language Model driven cognitive load prediction framework. This framework integrates comprehensive task context, domain knowledge, and behavior analysis to provide fast and cost-effective predictions in complex scenarios. By leveraging the capabilities of Large Language Models and employing reinforcement learning to model the cognitive load generation, our framework aims to offer real-time insights into human-related factors causing high cognitive load and predict cognitive levels over time, ultimately enhancing the performance of HSS teaming.

关键词： Large language models Reinforcement learning Human factors Predictive models cognitive load Real-time systems Robots Monitoring Load modeling Drones

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Fairness-Sensitive Policy-Gradient Reinforcement Learning for Reducing Bias in Robotic Assistance

Fairness-Sensitive Policy-Gradient Reinforcement Learning fo...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Jie Zhu Mengsha Hu Amy Zhang Ruoming Jin Rui Liu Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Kent State University Kent OH USA Department of Computer Science George Washington University Department of Computer Science Kent State University

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Robots assist humans in various activities, from daily living to collaborative manufacturing. Because they have biased learning sources (e.g., data, demonstrations, human feedback), robots inevitably have discriminatory performance regarding individual differences (e.g., skin color, mobility, appearance); discriminatory performance will undermine robots’ service quality, causes request ignorance and response delay, and even cause emotional offenses. Therefore, mitigating biases is critically important for delivering fair robotic services. In this paper, we design a bias-mitigation method – Fairness-Sensitive Policy Gradient Reinforcement Learning (FSPGRL), to help robots self-identify and correct biased behaviors. FSP-GRL identifies bias by examining the abnormal updates along particular gradients and updates the policy network to provide fair decisions. To validate FSPGRL’s effectiveness, we designed a human-centered service scenario: a robot serving people in a restaurant. With a user study involving 24 humans and 1,000 service demonstrations, FSPGRL has proven effective in maintaining fairness during robot services.

关键词： Image color analysis Design methodology Collaboration Reinforcement learning Skin Manufacturing Delays Robots

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HGIC: A Hand Gesture Based Interactive Control System for Efficient and Scalable Multi-UAV Operations

HGIC: A Hand Gesture Based Interactive Control System for Ef...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Mengsha Hu Jinzhou Li Runxiang Jin Chao Shi Lei Xu Rui Liu Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Kent State University Kent OH USA Department of Computer Science Kent State University Kent OH USA College of Engineering Cornell University Ithaca NY USA Department of Systems Science and Industrial Engineering Binghamton University Binghamton NY USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

As technological advancements continue to expand the capabilities of multi unmanned-aerial-vehicle systems (mUAV), human operators face challenges in scalability and efficiency due to the complex cognitive load and operations associated with motion adjustments and team coordination. Such cognitive demands limit the feasible size of mUAV teams and necessitate extensive operator training, impeding broader adoption. This paper developed a Hand Gesture Based Interactive Control (HGIC), a novel interface system that utilize computer vision techniques to intuitively translate hand gestures into modular commands for robot teaming. Through learning control models, these commands enable efficient and scalable mUAV motion control and adjustments. HGIC eliminates the need for specialized hardware and offers two key benefits: 1) Minimal training requirements through natural gestures; and 2) Enhanced scalability and efficiency via adaptable commands. By reducing the cognitive burden on operators, HGIC opens the door for more effective large-scale mUAV applications in complex, dynamic, and uncertain scenarios. HGIC will be open-sourced after the paper being published online for the research community, aiming to drive forward innovations in human-mUAV interactions.

关键词： Training Technological innovation Computer vision Scalability Robot kinematics Computational modeling Control systems Hardware Motion control Faces

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

Quantum Exploration-based Reinforcement Learning for Efficie...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Chao Huang Yibei Guo Zhihui Zhu Mei Si Daniel Blankenberg Rui Liu Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Kent State University Kent OH USA Department of Computer Science Ohio State University Columbus OH USA Department of Cognitive Science Rensselaer Polytechnic Institute Troy NY USA Center for Computational Life Sciences Cleveland Clinic Cleveland OH USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

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.

关键词： Training Quantum mechanics Stochastic processes Reinforcement learning Robot sensing systems Robot learning Collision avoidance Artificial intelligence Quantum circuit Robots

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Physics Representation Learning for Dexterous Manipulation Planning

Physics Representation Learning for Dexterous Manipulation P...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Yi Wu Mengsha Hu Runxiang Jin Rui Liu Informatique et Sciences du Numérique (Computer Research Laboratory) CentraléSupelec Paris-Saclay University Gif-Sur-Yvette France Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Department of Computer Science Kent State University Kent OH USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Dexterous manipulation in robotics, particularly with high degrees of freedom (DoF) devices like the 24-joint Shadow Hand, confronts complexities in search space and execution precision. Humans, however, manipulate objects effortlessly, thanks to their innate grasp of physics. Inspired by this, we introduce the Physics Representation Learning (PRL) framework for robotic hand manipulation. PRL uses physics principles for action conceptualization, such as aligning axes at specific angles (Figure 1). It is a natural language that describes the physics laws, thus bridging the gap between high-level semantics planning and joint-level execution through inverse kinematics (IK). Built on top of the physics-informed action space, PRL deploys a Reinforcement Learning (RL) network with expert demonstrations. Its effectiveness was validated in Nvidia Isaac Sim on four tasks: hammering, unscrewing, sweeping, and pinching. Results show that PRL significantly outperforms conventional joint-control algorithms and RL without demonstrations, underscoring the benefits of physics-based action representations for complex tasks.

关键词： Representation learning Semantics Natural languages Reinforcement learning Kinematics Planning Complexity theory Physics Robots

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Accessibility-Aware Reinforcement Learning for Inclusive Robotic Navigation

Accessibility-Aware Reinforcement Learning for Inclusive Rob...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Mengsha Hu Yunhuan Wu Yibei Guo Yi Wu Chao Shi Lei Xu Rui Liu Department of Computer Science Kent State University Kent OH USA Robotics Institute Carnegie Mellon University Pittsburgh PA USA Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Kent State University Kent OH USA Informatique et Sciences du Numérique (Computer Research Laboratory) CentraleSupélec Paris-Saclay University Gif-sur-Yvette France Department of Systems Science and Industrial Engineering Binghamton University Binghamton NY USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Robotic navigation aids humans in essential scenarios, including airport customer boarding, commercial meeting receptions, and disaster site navigation. While individuals benefit from robotic services, these services pose accessibility challenges for people with disabilities. Robots may face limitations in mobility, vision, hearing, and cognitive reasoning, hindering disabled individuals' access to these services. As robotic services become increasingly prevalent, ensuring they are inclusive and accessible is crucial, accommodating human disabilities with adaptations such as slower movements, standing support, and hazard detection. To address this issue, this study introduces a novel Accessibility-Aware Reinforcement Learning model (ARL). ARL extracts disability-related information from human behavioral observations using a contextembedding neural network. It then adjusts robot motions to provide inclusive assistance to individuals with disabilities. To assess the effectiveness of this approach, four assisting services in navigation scenarios ('lead', 'wait', 'assist', and 'protect') and three application scenarios involving navigating elderly individuals in a museum, through traffic, and across a building patio were designed. The results validate ARL's efficacy in enabling robots to make accessibility-aware decisions, thereby enhancing their support for individuals with disabilities.

关键词： Robot motion Navigation Neural networks Reinforcement learning Feature extraction People with disabilities Robot learning Data mining Older adults Robots

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Inner attention supported adaptive cooperation for heterogeneous multi robots teaming based on multi-agent reinforcement learning

arXiv

引用

arXiv 2020年

作者： Huang, Chao Liu, Rui Cognitive Robotics and AI Lab College of Aeronautics and Engineering Kent State University

Humans can selectively focus on different information based on different tasks requirements, other people's abilities and availability. Therefore, they can adapt quickly to a completely different and complex environments. If, like people, robot could obtain the same abilities, then it would greatly increase their adaptability to new and unexpected situations. Recent efforts in Heterogeneous Multi Robots Teaming have try to achieve this ability, such as the methods based on communication and multi-modal information fusion strategies. However, these methods will not only suffer from the exponential explosion problem with the increase of robots number but also need huge computational resources. To that end, we introduce an inner attention actor-critic method that replicates aspects of human flexibly cooperation. By bringing attention mechanism on computer vision, natural language process into the realm of multi-robot cooperation, our attention method is able to dynamically select which robots to attend to. In order to test the effectiveness of our proposed method, several simulation experiments have been designed. And the results show that inner attention mechanism can enable flexible cooperation and lower resources consuming in rescuing tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Industrial robots

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Unsupervised Learning of Part Similarity for Goal-Guided Accelerated Experiment Design in Metal Additive Manufacturing

arXiv

引用

arXiv 2023年

作者： Liu, Rui Liu, Sen Zhang, Xiaoli Cognitive Robotics and AI Lab College of Aeronautics and Engineering Kent State University KentOH44240 United States Department of Mechanical Engineering University of Louisiana at Lafayette LafayetteLA70503 United States Department of Mechanical Engineering Colorado School of Mines GoldenCO80401 United States Alliance for the Development of Additive Processing Technologies Colorado School of Mines GoldenCO80401 United States

Metal additive manufacturing (AM) is gaining broad interest and increasing use in the industrial and academic fields. However, the quantification and commercialization of standard parts usually require extensive experiments and expensive post-characterization, which impedes the rapid development and adaptation of metal AM technologies. In this work, a similarity-based acceleration (S-acceleration) method for design of experiments is developed using K-means clustering and Gaussian Mixture Model (GMM) unsupervised learning algorithms to reduce the time and costs associated with unveiling process-property (porosity defects) relationships during manufacturing. With S-acceleration, part semantic features from machine-setting parameters and physics-effects informed characteristics are explored for measuring mutual part similarities. A user-defined simplification rate of experiments is proposed to purposely remove redundant parts before conducting experiments printing without sacrificing information gain as original full factorial experiment design. This S-acceleration design of experiments is demonstrated on a Concept Laser M2 Dual-laser Cusing machine for the experimental plan of modeling relationships between process parameters and part porosity defects. The printed part has 2 mm diameter × 4 mm tall pin geometry considering variations in build location and orientation, laser settings and powder feedstock are held constant. In total, 242 parts are measured to create a "ground truth" data set of porosity levels by using X-ray tomography microscopy. The S-acceleration method is assessed for performance considering 40%, 50%, and 60% of user-defined experiment simplification rates. The repeated experiments are removed without ignoring the minority experiments outlier, assuring a similar process-property relation in the original experiment plan. The experiment number is significantly reduced based on part similarity with minimal compromise of model accuracy and obtained know

关键词： Design of experiments

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Trust Repair in Human-Swarm Teams+

Trust Repair in Human-Swarm Teams+

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Rui Liu Zekun Cai Michael Lewis Joseph Lyons Katia Sycara Cognitive Robotics and AI Lab College of Aeronautics and Engineering Kent State University OH USA School of Computing and Information University of Pittsburgh Air Force Research Laboratory Wright-Patterson AFB Dayton OH USA Robotics Institute School of Computer Science Carnegie Mellon University PA USA

ISBN: (纸本)9781728126234

Swarm robots are coordinated via simple control laws to generate emergent behaviors such as flocking, rendezvous, and deployment. Human-swarm teaming has been widely proposed for scenarios, such as human-supervised teams of unmanned aerial vehicles (UAV) for disaster rescue, UAV and ground vehicle cooperation for building security, and soldier-UAV teaming in combat. Effective cooperation requires an appropriate level of trust, between a human and a swarm. When an UAV swarm is deployed in a real-world environment, its performance is subject to real-world factors, such as system reliability and wind disturbances. Degraded performance of a robot can cause undesired swarm behaviors, decreasing human trust. This loss of trust, in turn, can trigger human intervention in UAVs' task executions, decreasing cooperation effectiveness if inappropriate. Therefore, to promote effective cooperation we propose and test a trust-repairing method (Trust-repair) restoring performance and human trust in the swarm to an appropriate level by correcting undesired swarm behaviors. Faulty swarms caused by both external and internal factors were simulated to evaluate the performance of the Trust-repair algorithm in repairing swarm performance and restoring human trust. Results show that Trust-repair is effective in restoring trust to a level intermediate between normal and faulty conditions.

关键词： dike swarms Nonprofit Organizations trust Trust Unmanned Aerial Vehicle drone honey bees control law Human Robot Swarming behavior

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Roadmap for Animate Matter

arXiv

引用

arXiv 2024年

作者： Volpe, Giorgio Araújo, Nuno A.M. Guix, Maria Miodownik, Mark Martin, Nicolas Alvarez, Laura Simmchen, Juliane Leonardo, Roberto Di Pellicciotta, Nicola Martinet, Quentin Palacci, Jérémie Ng, Wai Kit Saxena, Dhruv Sapienza, Riccardo Nadine, Sara Mano, João F. Mahdavi, Reza Adiels, Caroline Beck Forth, Joe Santangelo, Christian Palagi, Stefano Seok, Ji Min Webster-Wood, Victoria A. Wang, Shuhong Yao, Lining Aghakhani, Amirreza Barois, Thomas Kellay, Hamid Coulais, Corentin van Hecke, Martin Pierce, Christopher J. Wang, Tianyu Chong, Baxi Goldman, Daniel I. Reina, Andreagiovanni Trianni, Vito Volpe, Giovanni Beckett, Richard Nair, Sean P. Armstrong, Rachel Department of Chemistry University College London 20 Gordon Street LondonWC1H 0AJ United Kingdom Departamento de Física Faculdade de Ciências Universidade de Lisboa Lisboa1749-016 Portugal Centro de Física Teórica e Computacional Faculdade de Ciências Universidade de Lisboa Lisboa1749-016 Portugal Department of Materials Science and Physical Chemistry Institute of Theoretical and Computational Chemistry University of Barcelona Barcelona Catalonia08028 Spain Department of Mechanical Engineering University College London London United Kingdom University of Bordeaux CNRS Centre de Recherche Paul Pascal UMR 5031 115 Avenue du Dr. Albert Schweitzer Pessac33600 France Pure and Applied Chemistry University of Strathclyde United Kingdom Department of Physics "Sapienza" University of Rome Rome00185 Italy NANOTEC-CNR Institute of Nanotechnology Soft and Living Matter Laboratory Rome00185 Italy Klosterneuburg Austria Blackett Laboratory Department of Physics Imperial College London London United Kingdom CICECO - Aveiro Institute of Materials Department of Chemistry University of Aveiro Campus Universitário de Santiago Aveiro3810-193 Portugal Department of Physics University of Gothenburg GothenburgSE-41296 Sweden Department of Physics University of Liverpool Crown Street LiverpoolL69 7ZD United Kingdom Department of Chemistry University of Liverpool Oxford Street LiverpoolL69 7ZE United Kingdom Physics Department Syracuse University SyracuseNY13244 United States The BioRobotics Institute Department of Excellence in Robotics & AI Sant'Anna School of Advanced Studies Tuscany Pisa56025 Italy Department of Mechanical Engineering Carnegie Mellon University United States Department of Mechanical Engineering Department of Biomedical Engineering The Robotics Institute Carnegie Mellon University United States Morphing Matter Lab Mechanical Engineering UC Berkeley United States Zhejiang University China Institute of Biomaterials and Biomolecular

Humanity has long sought inspiration from nature to innovate materials and devices. As science advances, nature-inspired materials are becoming part of our lives. Animate materials, characterized by their activity, adaptability, and autonomy, emulate properties of living systems. While only biological materials fully embody these principles, artificial versions are advancing rapidly, promising transformative impacts across various sectors. This roadmap presents authoritative perspectives on animate materials across different disciplines and scales, highlighting their interdisciplinary nature and potential applications in diverse fields including nanotechnology, robotics and the built environment. It underscores the need for concerted efforts to address shared challenges such as complexity management, scalability, evolvability, interdisciplinary collaboration, and ethical and environmental considerations. The framework defined by classifying materials based on their level of animacy can guide this emerging field encouraging cooperation and responsible development. By unravelling the mysteries of living matter and leveraging its principles, we can design materials and systems that will transform our world in a more sustainable manner. Copyright © 2024, The Authors. All rights reserved.

关键词： Biotic

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