Artificial Life and Robotics最新文献

{"title":"Synergistic development model of population growth and infrastructure networks based on the slime mold network","authors":"Megumi Uza,&nbsp;Airi Kinjo,&nbsp;Itsuki Kunita","doi":"10.1007/s10015-025-01035-z","DOIUrl":"10.1007/s10015-025-01035-z","url":null,"abstract":"<div><p>Developing efficient transportation infrastructure networks capable of accommodating increases in population and demand is essential in urban planning. The conventional approaches to urban planning involve simulations using mathematical models that incorporate temporal changes. The current models are often based on static factors like existing land and road networks. However, land use and road networks need to be adapted to environmental and systemic changes to better capture urban dynamics. In this study, we aimed to address this by proposing a novel synergistic development model of population growth and infrastructure networks inspired by the adaptive network formation of slime mold <i>Physarum polycephalum</i>. The proposed model builds on the Physarum solver by incorporating two dynamic processes: adding new source points and deleting sink points with low flow. Adding source points simulates population growth and increases infrastructure demand, whereas deleting sink points enhances network efficiency by removing redundant paths. The numerical simulations were conducted under various conditions to evaluate the effect of these processes on network formation. The results indicate that deleting sink points accelerates the convergence of the network by eliminating unnecessary paths. However, an increased flow can result in higher energy loss if the number of paths is insufficient. These findings indicate that adaptive feedback mechanisms, inspired by biological systems, play a crucial role in optimizing infrastructure networks in response to population growth, offering insights for flexible urban development strategies.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"30 3","pages":"523 - 533"},"PeriodicalIF":0.8,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145168109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Target specific multi-image 3D scrambling algorithm for security cameras","authors":"Abhijeet Ravankar,&nbsp;Arpit Rawankar,&nbsp;Ankit A. Ravankar","doi":"10.1007/s10015-025-01033-1","DOIUrl":"10.1007/s10015-025-01033-1","url":null,"abstract":"<div><p>With the proliferation of security cameras, image content protection is a major challenge. Image scrambling has increasingly been used for content protection as it does not degrade the quality of image. However, security cameras pose challenges of real-time implementation and target specific content protection. To this end, this paper presents a target specific, linear transform-based multi-image scrambling algorithm. The algorithm can scramble the image in 2D and 3D. Scrambling in 3D enables inter-image pixel scrambling which prevents brute-force attacks. The algorithm can be implemented using MMA (matrix–matrix multiply add) operation for parallel computing. A faster algorithm is proposed for serial computation. Both square and rectangular images can be scrambled. Along with complete image, targeted areas of the image can be scrambled in real time. The quality of scrambling is evaluated using PSNR (peak-signal-to-noise ratio) parameter. Experiment results with actual security cameras with motion detection feature shows that the proposed algorithm can be used in real time with high pixel irregularity for content protection.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"30 3","pages":"372 - 382"},"PeriodicalIF":0.8,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145165510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robots reading recipes: large language models as translators between humans and machines","authors":"Oliver Wang,&nbsp;Grant Cheng,&nbsp;Luc Caspar,&nbsp;Akira Yokota,&nbsp;Mahdi Khosravy,&nbsp;Olaf Witkowski","doi":"10.1007/s10015-025-01031-3","DOIUrl":"10.1007/s10015-025-01031-3","url":null,"abstract":"<div><p>Large Language Models (LLMs) are a type of machine learning model trained on vast amounts of natural language that have demonstrated novel capabilities in tasks such as text prediction and generation. These tasks allow LLMs to be remarkably suited for understanding the semantics of natural language, which in turn enables applications such as planning real world tasks, writing code for computers, and translating between human languages. Even though LLMs could provide more flexibility in interpreting user requests and have shown to possess some commonsense knowledge, their capabilities for translating natural language instructions into code to control robot actions is only starting to be explored. More specifically, in this paper we are interested in the control of robots tasked with preparing cocktails. Within this context, it is assumed that the LLM has access to a repository of well-formatted recipes. This means that each recipe is written according to the following layout: a list of ingredients, then a subsequent description of how to prepare and mix the various items. Moreover, a set of low-level modules responsible for robot manipulation and vision-related tasks is also provided to the LLM in the shape of an application programming interface (API). Consequently, the main focus of the LLM is on generating a sequence of calls to the API, along with the right parameters, to produce the cocktail requested by users in natural language. Here, we show that it is feasible for LLMs to perform this type of translation on a small number of custom modules, and that certain techniques provide a measurable benefit to the accuracy and consistency of this task without fine-tuning. We found in particular that the use of an ensemble-voting strategy, where multiple trials are repeated and the most common answer is selected, increases accuracy to a certain extent. In addition, there is moderate support for the use of natural language parsing to adjust the prompt of the LLM prior to translation. Lastly, building on previous knowledge we also provide a set of guidelines to help design prompts to improve the accuracy of the resulting sequence of actions. In general, these results suggest that while LLMs can be used as translators of robot instructions, they are best applied in conjunction with these other strategies. The impact of these findings could influence future robotics development, as it provides directions for implementing LLMs more effectively and broadening the accessibility of robotic control to users without an extensive software background.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"30 3","pages":"407 - 416"},"PeriodicalIF":0.8,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10015-025-01031-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145165228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novelty-based multi-objectivization for unbounded search space optimization","authors":"Ryuki Ishizawa,&nbsp;Hiroyuki Sato,&nbsp;Keiki Takadama","doi":"10.1007/s10015-025-01034-0","DOIUrl":"10.1007/s10015-025-01034-0","url":null,"abstract":"<div><p>Unlike the conventional swarm or evolutionary optimizations that are generally assumed the “pre-defined” bounded search space, this paper addresses the optimization for the “unbounded” search space. For this purpose, this paper proposes novelty-based multi-objectivization with local and rough area search (NM-LRS), which adds the novelty criterion in the given optimization criteria to roughly search the unbounded search space for obtaining the “potential area” where the optimal solution is most likely located and then searches the “potential area” to find the optimal solution by a local area search. To investigate the effectiveness of the proposed methods, the experiment compares the proposed methods with the conventional optimization methods for the unbounded multi-modal optimization and has revealed the following implications: (i) the peak ratio (<i>i</i>.<i>e</i>., the ratio of the founded peaks of the multi-modal function) of NM-LRS is higher than that of the conventional methods; and (ii) NM-LRS is robust for the location of the initial search area in the most functions.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"30 3","pages":"383 - 397"},"PeriodicalIF":0.8,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10015-025-01034-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145164142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experiments on resolved acceleration control of a 3-link dual-arm underwater robot with model error compensator","authors":"Reo Nishio,&nbsp;Yuta Hanazawa,&nbsp;Shinichi Sagara,&nbsp;Radzi Bin Ambar","doi":"10.1007/s10015-025-01032-2","DOIUrl":"10.1007/s10015-025-01032-2","url":null,"abstract":"<div><p>Underwater environments provide significant challenges for humans, thus researchers have focused on controlling underwater robots equipped with manipulators known as Underwater Vehicle-Manipulator System (UVMS) that perform underwater tasks instead of humans. To achieve high-precision control of UVMS, an accurate mathematical model must be developed. However, there are modeling errors between the UVMS model used for control system and the fluid forces that actually act on the robot. In conventional studies, control methods based on joint space have been used as a compensation controller for disturbances, including modeling errors. This paper proposes a Resolved Acceleration Control (RAC) method for UVMS that incorporates a Model Error Compensator (MEC), a control method based on task space, designed to minimize these model errors. The proposed method aims to achieve robust trajectory tracking control for UVMS by suppressing the uncertainties in modeling of fluid forces and the effects of disturbances. Furthermore, unlike many prior studies that demonstrate the effectiveness of their methods through simulations, this study validates the proposed method through position control experiments of a robot under wave disturbances. The experimental results confirm the robustness of the control system against modeling errors and wave disturbances, demonstrating the usefulness of the proposed method.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"30 3","pages":"512 - 522"},"PeriodicalIF":0.8,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10015-025-01032-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145163393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a brain–machine interface based robot navigation system for disabled people","authors":"Abhijeet Ravankar,&nbsp;Ankit A. Ravankar,&nbsp;Arpit Rawankar","doi":"10.1007/s10015-025-01024-2","DOIUrl":"10.1007/s10015-025-01024-2","url":null,"abstract":"<div><p>People with serious physical disabilities (ex. spinal muscular atrophy) find it difficult to control a robot wheelchair. Although gesture-based robot control mechanisms have been proposed, making such gestures is not always feasible. To this end, this paper proposes a brain–machine interface (BMI) for robot control by processing electroencephalograph (EEG) signals captured from non-invasive external device. We systematically process the EEG signals to first estimate the most prominent brain channels. This eliminates the redundant information or noise which adversely influences the recognition accuracy. We then estimate the most prominent EEG waves among the prominent channels. Later, the combination of prominent brain waves among the prominent channels which gives the most accurate robot control are estimated. Convolutional neural network (CNN) is used to process the EEG signals. The user can control the robot in four different directions. Experiments with actual external BMI device are performed and robot is controlled.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"30 3","pages":"398 - 406"},"PeriodicalIF":0.8,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145169623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proposal for improving SimCLR using image synthesis for defect recognition tasks","authors":"Hirohisa Kato,&nbsp;Fusaomi Nagata","doi":"10.1007/s10015-025-01028-y","DOIUrl":"10.1007/s10015-025-01028-y","url":null,"abstract":"<div><p>This paper proposes an improvement of SimCLR for defect recognition tasks by image synthesis using weighted averages. There are studies on applying contrastive learning to defect detection in industrial products. This is because the number of defective products is quite small compared to non-defective products, and contrastive learning is a method that allows you to train a model with a small dataset by augmenting images and comparing them. However, problems with random trimming have been reported for the combination of defect detection and contrastive learning. Since defect images consist of defect areas and non-defect areas, augmentation by random cropping does not work well. To solve this problem, this study proposes the addition of image synthesis using weighted averaging to the conventional SimCLR’s augmentation method. The proposed method avoids wasteful learning that attracts feature vectors between cropped defect and non-defect areas. In the experiment, a CNN was trained on a small dataset of 32 images, and our proposed method improved AUC by 15% compared to the conventional method.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"30 3","pages":"432 - 438"},"PeriodicalIF":0.8,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10015-025-01028-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145168685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"6D NewtonianVAE: 6-DoF object pose estimation and control method for robotic tasks via learning from multi-view visual information","authors":"Mai Terashima,&nbsp;Ryo Okumura,&nbsp;Pedro Miguel Uriguen Eljuri,&nbsp;Katsuyoshi Maeyama,&nbsp;Yuanyuan Jia,&nbsp;Tadahiro Taniguchi","doi":"10.1007/s10015-025-01026-0","DOIUrl":"10.1007/s10015-025-01026-0","url":null,"abstract":"<div><p>In this study, we propose a method for learning a latent space representing 6-DoF poses and performing 6-DoF control in the latent space using NewtonianVAE. NewtonianVAE, a type of world models based on Variational Autoencoder (VAE), can learn the dynamics of the environment as a latent space from observational data and perform proportional control based on the estimated position on the latent space. However, previous research has not demonstrated 6-DoF pose estimation and control using NewtonianVAE. Therefore, we propose 6D NewtonianVAE, which extends the latent space by incorporating the rotation vector to construct the latent space representing 6-DoF poses and perform 6-DoF control based on the estimated poses. Experimental results showed that our method achieves 6-DoF control with an accuracy within 7 mm and 0.02 rad in a real-world. It was also shown that 6-DoF control is possible even in unseen environments. Our approach enables end-to-end 6-DoF pose estimation and control without annotated data. It also eliminates the need for RGB-D or point cloud data and relies solely on RGB images, reducing implementation and computational costs.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"30 3","pages":"472 - 483"},"PeriodicalIF":0.8,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10015-025-01026-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145168299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variance control for black box variational inference using the James–Stein estimator","authors":"Dominic B. Dayta,&nbsp;Takatomi Kubo,&nbsp;Kazushi Ikeda","doi":"10.1007/s10015-025-01030-4","DOIUrl":"10.1007/s10015-025-01030-4","url":null,"abstract":"<div><p>Black box variational inference is a promising framework in a succession of recent efforts to make Variational Inference more “black box”. However, in its basic version it either fails to converge due to instability or requires some fine-tuning of the update steps prior to execution that hinders it from being completely general purpose. We propose a method for regulating its parameter updates by re-framing stochastic optimization as a multivariate estimation problem. Borrowing from estimation theory, we examine the properties of the James–Stein estimator as a replacement for the arithmetic mean of Monte Carlo estimates of the gradient of the evidence lower bound. Theoretical guarantees for its variance reduction properties are also given. We show through simulations that the proposed method provides relatively weaker variance reduction than Rao-Blackwellization, but offers a tradeoff of being simpler and requiring no prior analysis on the part of the user. Comparisons on benchmark datasets also demonstrate a consistent performance at par or better than the Rao-Blackwellized approach in terms of resulting model fit.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"30 3","pages":"365 - 371"},"PeriodicalIF":0.8,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145164230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dependence of Péclet number on agent-based chemotactic predator–prey system","authors":"Chikoo Oosawa","doi":"10.1007/s10015-025-01029-x","DOIUrl":"10.1007/s10015-025-01029-x","url":null,"abstract":"<div><p>Here, we concentrate on the world that only chemicals are allowed to use as cues from agents, the chemicals secreted from all agents, diffuse and decay under fluid conditions, give rise to change of motility to agents, that is called chemotaxis. At first, motility of single agent is confirmed, and then we show a simple mechanism of predator (chaser)–prey (target) system consist of such chemotactic agents only. Finally, we explicitly consider fluid conditions in the system. The model system has parameter <span>(alpha)</span>, corresponding diffusion coefficient of the chemicals, inversely relates to Péclet numbers. The smaller Péclet numbers give rise to more obscure chemical traces, but leading to higher survivability-efficient to predator (chaser) as well as prey (target), indicating that they can use complex traces to change their moving directions without using any waves, such as electromagnetic and/or sound. These results can be regarded as an emergence phenomena of diffusion- and chemotaxis-driven swarm intelligence.\u0000</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"30 3","pages":"458 - 464"},"PeriodicalIF":0.8,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10015-025-01029-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145164862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}