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A methodical interpretation of adaptive robotics: Study and reformulation

NEUROCOMPUTING 2022年 512卷 381-397页

作者： Enayati, Amir M. Soufi Zhang, Zengjie Najjaran, Homayoun Univ Victoria Victoria BC V8P 5C2 Canada Univ British Columbia Kelowna BC V1V 1V7 Canada

The recent development of industrial manufacturing and social services has witnessed a significant trend of automation and intelligentization due to the wide application of robots and the technology of artificial intelligence (AI). While robots liberate humans from tedious and dangerous work in hazardous environ-ments, AI simplifies the programming of robots by automatically inferring patterns and models from the interaction between the robots and the environment. Nevertheless, the application of robots and AI to more general manufacturing and social tasks is still limited by the lack of flexibility and adaptability to the changes in the task and the environment. Thus, a new concept, adaptive robotics, has been pro-posed to address the desire that an AI-powered robot should be able to properly reprogram itself to these changes without human intervention. Nevertheless, this concept is yet too abstract to provide any speci-fic guidance to the development of robot programs. In this paper, we attempt to provide methodical redefinition and reformulation of adaptive robotics both in conceptual and mathematical manners based on the study of previous results. First of all, we introduce the essential motivation and the conceptual origination of adaptability of mechanical systems. Then, we review the previous literature and explore the related work of adaptive robotics. Based on this, we provide a uniform mathematical formulation of adaptive robotics based on adaptive and robust Markov-decision process (MDP). Through this work, we attempt to inspire the generic framework of adaptive robotics incorporating the existing immature paradigms, by which we are aiming at a clarified and well-defined context of adaptive robotics for future research on related domains.(c) 2022 Elsevier B.V. All rights reserved.

关键词： adaptive robotics Artificial Intelligence Machine Adaptability Machine Learning Markov Decision Process Industry 4 0

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Self-Maintainable Electronic Materials with Skin-Like Characteristics Enabled by Graphene-PEDOT:PSS Fillers

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ADVANCED SCIENCE 2025年

作者： Alehosseini, Morteza Kadumudi, Firoz Babu Revesz, Sinziana Reikandeh, Parham Karimi Henriksen, Jonas Rosager Zsurzsan, Tiberiu-Gabriel Spangenberg, Jon Dolatshahi-Pirouz, Alireza Tech Univ Denmark Dept Hlth Technol DK-2800 Lyngby Denmark Tech Univ Denmark DTU Dept Elect & Photon Engn DK-2800 Lyngby Denmark Tech Univ Denmark Dept Civil & Mech Engn DK-2800 Lyngby Denmark

Conventional devices lack the adaptability and responsiveness inherent in the design of nature. Therefore, they cannot autonomously maintain themselves in natural environments. This limitation is primarily because of using rigid and fragile material components for their construction, which hinders their ability to adapt and evolve in changing environments. Moreover, they often cannot self-repair after injuries or significant damage. Even devices with self-healing, soft, and responsive properties often fail to seamlessly integrate all these attributes into a single, scalable, and cohesive platform. In this study, a significant breakthrough is introduced by utilizing graphene-poly(3,4-ethylenedioxythiophene): polystyrene sulfonate (graphene-PEDOT:PSS) fillers to transform a typically weak, insulating, and jelly-like material into a soft electronic material with properties akin to those of living organisms, such as skin tissue. The developed electronic materials exhibit a range of other capabilities attributed to the hierarchical organization originating from filler enhancement, which includes methods such as heat regulation, 3D printability, and multiplex sensing. The introduction of this new class of materials can facilitate the self-maintenance of life-like soft robots and bioelectronics that can be seamlessly integrated within dynamic environments, such as the human body, while demonstrating the ability to sense, respond, and adapt to challenging environments.

关键词： adaptive robotics bioelectronics graphene-PEDOT self-healing materials soft electronics

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Graph-based meta-learning for context-aware sensor management in nonlinear safety-critical environments

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ADVANCED robotics 2024年第6期38卷 368-385页

作者： O'Hara, Christopher Aaron Yairi, Takehisa Univ Tokyo Grad Sch Engn Dept Aeronaut & Astronaut Tokyo Japan

This study introduces a novel framework for optimizing energy efficiency and computational load in safety-critical robotic systems operating in nonlinear domains. Leveraging Graph Attention Networks for state awareness and decision-making, the framework employs adaptive sensor and filter toggling strategies to dynamically manage system resources through real-time inferential processes. Our framework maintains continuous robot operation in the presence of sensor noise and environmental disturbances by activating additional sensors, thus preventing system shutdowns or stalls. Few-shot meta-learning techniques further augment the model's adaptability, allowing it to generalize and make real-time decisions across varying operational conditions. An extensive evaluation reveals a reduced average energy consumption, compared to 'always-on' configurations, by 13.71% and CPU utilization by 29.07%, without compromising system performance and safety. We also introduce Matching Networks and Siamese Networks with different loss functions to assess the system's capability to adapt to different levels of criticality. Our experiments demonstrate that the system prioritizes performance and safety in high-critical scenarios while maximizing energy efficiency in less critical situations. The framework's real-time decision-making capability is particularly crucial in human-robot environments and holds significant implications for future applications in nonlinear control systems and resilient robotic systems. [GRAPHICS] .

关键词： adaptive robotics nonlinear control meta-Learning sensor management human-robot interaction

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The Effectiveness of Niching on OpenAI-Evolution Strategies in the Evolution of Robotic Behavior

The Effectiveness of Niching on OpenAI-Evolution Strategies ...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Bianchini, Arthur Holtrup Machado, Brenda Silva Carvalho, Jonata Tyska Univ Fed Santa Catarina Florianopolis SC Brazil

ISBN: (纸本)9798400701207

In Evolutionary robotics, techniques inspired by biological evolution are used to evolve robotic morphology and behaviors. Evolution Strategies is a popular algorithm in this field due to its capability to perform continuous optimization. In evolutionary robotics, the use of approaches as, for instance, niching techniques and environmental variation, has shown promising results in order to develop more effective adaptive behaviors or even improve the convergence time. The objective of this work is to analyze how a recent adaptation of Evolution Strategies developed by OpenAI, which we call OpenAI-ES, behaves when working alongside environmental variation and niching techniques. The results showed that this approach applied to the double pole balancing benchmark can improve performance by 9% for OpenAI-ES.

关键词： Evolution Strategies Niche Evolution adaptive robotics

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Sim2Real Grasp Pose Estimation for adaptive Robotic Applications 22

Sim2Real Grasp Pose Estimation for Adaptive Robotic Applicat...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Horvath, Daniel Bocsi, Kristof Erdos, Gabor Istenes, Zoltan Eotvos Lorand Res Network Ctr Excellence Prod Informat & Control Inst Comp Sci & Control Budapest Hungary Eotvos Lorand Univ CoLocat Ctr Acad & Ind Cooperat Budapest Hungary Budapest Univ Technol & Econ Dept Mfg Sci & Engn Budapest Hungary

ISBN: (纸本)9781713872344

adaptive robotics plays an essential role in achieving truly co-creative cyber physical systems. In robotic manipulation tasks, one of the biggest challenges is to estimate the pose of given workpieces. Even though the recent deep-learning-based models show promising results, they require an immense dataset for training. In this paper, two vision-based, multi-object grasp pose estimation models (MOGPE), the MOGPE Real-Time and the MOGPE High-Precision are proposed. Furthermore, a sim2real method based on domain randomization to diminish the reality gap and overcome the data shortage. Our methods yielded an 80% and a 96.67% success rate in a real-world robotic pick-and-place experiment, with the MOGPE Real-Time and the MOGPE High-Precision model respectively. Our framework provides an industrial tool for fast data generation and model training and requires minimal domain-specific data. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： adaptive robotics robot vision sim2real knowledge transfer smart manufacturing cyber physical production systems

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Predicting apparent personality from body language: benchmarking deep learning architectures for adaptive social human-robot interaction

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ADVANCED robotics 2021年第19期35卷 1167-1179页

作者： Romeo, Marta Garcia, Daniel Hernandez Han, Ting Cangelosi, Angelo Jokinen, Kristiina Univ Manchester Dept Comp Sci Manchester Lancs England AIST Tokyo Artificial Intelligence Res Ctr AIRC Tokyo Japan Herriot Watt Univ Sch Math & Comp Sci Edinburgh Midlothian Scotland

First impressions of personality traits can be inferred by non-verbal behaviours such as head pose, body postures, and hand gestures. Enabling social robots to infer the apparent personalities of their users based on such non-verbal cues will allow robots to gain the ability of adapting to their users, constituting a further step towards the personalisation of human-robot interactions. Deep learning architectures such as residual networks, 3D convolutional networks, and long-short time memory networks have been applied to classify human activities and actions in computer vision tasks. These same architectures are beginning to be applied to study human emotions and personality by focusing mainly on facial features in video recordings. In this work, we exploit body language cues to predict apparent personality traits for human-robot interactions. We customised four state-of-the-art neural network architectures to the task, and benchmarked them on a dataset of short side-view videos of dyadic interactions. Our results show the potential for deep learning architectures to predict apparent personality traits from body language cues. While the performance varied between models and personality traits, our results show that these models could still be able to predict sole personality traits, as exemplified by the results on the conscientiousness trait.

关键词： Personality computing deep learning video classification adaptive robotics

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On the Performance of Online Adaptation of Robots Controlled by Nanowire Networks

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IEEE ACCESS 2023年 11卷 144408-144420页

作者： Baldini, Paolo Roli, Andrea Braccini, Michele Alma Mater Studiorum Univ Bologna Dept Comp Sci & Engn DISI Cesena Campus I-47521 Cesena Italy European Ctr Living Technol I-30123 Venice Italy

In order to complete useful tasks in complex and changing contexts, robots need to be able to adapt their behavior or actions. This requires the ability to find effective approaches to the situation at hand, and to maintain them until they keep being effective. The final goal can be summarized by the will of maintaining a high performance during the whole life of the robot, regardless of the changes that may intervene during its activity. In this work, we evaluate by the point of view of the resulting life-long performance two methodologies for the adaptation of robots controlled by immutable network-based control systems: the Nanowire Networks. We demonstrate that modifying the best found solution leads to constant improvement and to overall better cumulative performance. Complementarily, we show that a less constrained approach simplifies the exploration of different behaviors but reduces life-long performance. Finally, we confirm previous results suggesting the potential of using this novel neuromorphic device (i.e., the Nanowire Network) for the control of robots.

关键词： Robots Behavioral sciences Robot sensing systems Sensors Face recognition Control systems Training Life estimation Nanowires Life-long adaptation online adaptation adaptive robotics nanowire network

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Sim2Real Grasp Pose Estimation for adaptive Robotic Applications

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IFAC-PapersOnLine 2023年第2期56卷 5233-5239页

作者： Dániel Horváth Kristóf Bocsi Gábor Erdős Zoltán Istenes Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Eőtvős Loránd Research Network Budapest Hungary CoLocation Center for Academic and Industrial Cooperation Eőtvős Loránd University Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

关键词： adaptive robotics robot vision sim2real knowledge transfer smart manufacturing cyber physical production systems

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QR Code-Based Self-Calibration for a Fault-Tolerant Industrial Robot Arm

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IEEE ACCESS 2019年 7卷 73349-73356页

作者： Zhang, Yizheng Zhu, Wangshu Rosendo, Andre ShanghaiTech Univ Sch Informat Sci & Technol Dept Comp Sci Living Machines Labs Shanghai 201210 Peoples R China

Malfunctions on industrial robots can cost factories 22 000 dollars per minute. Although the benefits of a fault-tolerant robot arm are clear, redundant sensors would steeply add to the costs of such robots while machine learning-based methods would spend too much time learning the robot's model. We propose a simple but highly effective method to infer which joint underwent failure and at which angle this joint is constrained and to, then, modify the inverse kinematics (IK) algorithm to adaptively achieve the goal. Our method involves combining the robot arm with a QR code and an inexpensive camera, building a virtual link between these three to give the relative position of the end-effector. Once one joint/encoder/motor suffers damage, we use this virtual link to calibrate this joint by coordinate transformation, to calculate the constrained angle, and to recalculate the trajectory through IK iterations with the Newton-Raphson method. We prove the efficacy of our method with pick-and-place experiments, commonly seen in industrial settings, emulating malfunctions on different joints and at different angles, and our method can successfully finish the task in most cases. We further demonstrate that our method is capable, for almost all of the six-degree-off-reedom manipulators, to adapt to joint failures after suffering an actuator failure. With the steep increase of robots within factories, this paper presents an elegant approach to keep robots functional until maintenance is scheduled, reducing downtime.

关键词： Fault tolerance calibration adaptive robotics QR code robot arm

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PRIveT - a portable ubiquitous robotics testbed for adaptive human-robot interaction

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JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS 2016年第1期8卷 5-19页

作者： Sandygulova, Anara Dragone, Mauro O'Hare, Gregory M. P. Nazarbayev Univ Sch Sci & Technol Astana Kazakhstan Univ Coll Dublin Sch Comp Sci & Informat Dublin 4 Ireland Trinity Coll Dublin Dublin Ireland Univ Coll Dublin Earth Inst Dublin 4 Ireland

Ubiquitous robots are robots integrated with smart environments, where they cooperate with networks of heterogeneous sensors to achieve complex tasks. Their successful application opens important research questions for both their engineering and their interaction with human users, especially in public space scenarios, when they need to interact in a socially acceptable manner with multiple and previously unknown people. This article presents a testbed, named PRIveT, which is purposefully designed to support effective human-robot interaction (HRI) in public spaces. The PRIveT testbed consists of an ubiquitous robotic system that is able to autonomously engage and adapt to its users. This article describes the design rationale of the PRIveT testbed and its technical features, and presents four research studies that have used the testbed extensively. Reflections on the testbed features that were important in these studies and lessons learnt are also discussed. An assessment of the PRIveT testbed in terms of utility of the design for human-robot interaction experiments concludes the paper.

关键词： Human-robot interaction ubiquitous robotics testbed adaptive robotics human perception

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