Andreea Tulbure, Carmen Scheidemann, Elias Steiner
et al.
Task-oriented handovers (TOH) are fundamental to effective human-robot collaboration, requiring robots to present objects in a way that supports the human's intended post-handover use. Existing approaches are typically based on object- or task-specific affordances, but their ability to generalize to novel scenarios is limited. To address this gap, we present AFT-Handover, a framework that integrates large language model (LLM)-driven affordance reasoning with efficient texture-based affordance transfer to achieve zero-shot, generalizable TOH. Given a novel object-task pair, the method retrieves a proxy exemplar from a database, establishes part-level correspondences via LLM reasoning, and texturizes affordances for feature-based point cloud transfer. We evaluate AFT-Handover across diverse task-object pairs, showing improved handover success rates and stronger generalization compared to baselines. In a comparative user study, our framework is significantly preferred over the current state-of-the-art, effectively reducing human regrasping before tool use. Finally, we demonstrate TOH on legged manipulators, highlighting the potential of our framework for real-world robot-human handovers.
Large Language Models (LLMs) are increasingly used to power autonomous agents for complex, multi-step tasks. However, human-agent interaction remains pointwise and reactive: users approve or correct individual actions to mitigate immediate risks, without visibility into subsequent consequences. This forces users to mentally simulate long-term effects, a cognitively demanding and often inaccurate process. Users have control over individual steps but lack the foresight to make informed decisions. We argue that effective collaboration requires foresight, not just control. We propose simulation-in-the-loop, an interaction paradigm that enables users and agents to explore simulated future trajectories before committing to decisions. Simulation transforms intervention from reactive guesswork into informed exploration, while helping users discover latent constraints and preferences along the way. This perspective paper characterizes the limitations of current paradigms, introduces a conceptual framework for simulation-based collaboration, and illustrates its potential through concrete human-agent collaboration scenarios.
Partnerships between corporations and local communities play a crucial role in promoting inclusive and sustainable development, especially in areas directly affected by industrial activities. However, the implementation of such partnerships often fails to reflect the principles of equality, transparency, and active community participation. This study is part of a community service initiative aimed at analyzing the partnership model between PT Alam Sinar and the local community in Gampingan Village, Pagak Subdistrict, Malang Regency. It also evaluates the benefits, supporting factors, and challenges of the partnership by comparing conditions before and after the program was implemented. This research employs a qualitative approach with a case study design. Data were collected through observation, in-depth interviews, and documentation. Informants were selected purposively, including the village head, CSR representatives from the company, and community beneficiaries. The community service team acted as facilitators during the assessment process, participatory discussions, and reflection sessions involving both residents and the company. Findings indicate that the partnership is implemented through corporate social responsibility (CSR) programs focused on skills training, job creation, waste management, and the development of basic infrastructure. The benefits observed cover economic, social, and environmental aspects. Nevertheless, several challenges remain, such as limited community involvement in decision-making, suboptimal communication, and a lack of support from the district government. This study recommends strengthening community capacity, optimizing the role of village authorities, and developing a more inclusive, adaptive partnership model that responds to the actual needs of the local population.
Debora Firmino de Souza, Sonia Sousa, Kadri Kristjuhan-Ling
et al.
The Industry 5.0 transition highlights EU efforts to design intelligent devices that can work alongside humans to enhance human capabilities, and such vision aligns with user preferences and needs to feel safe while collaborating with such systems take priority. This demands a human-centric research vision and requires a societal and educational shift in how we perceive technological advancements. To better understand this perspective, we conducted a systematic literature review focusing on understanding how trust and trustworthiness can be key aspects of supporting this move towards Industry 5.0. This review aims to overview the most common methodologies and measurements and collect insights about barriers and facilitators for fostering trustworthy HRI. After a rigorous quality assessment following the Systematic Reviews and Meta-Analyses guidelines, using rigorous inclusion criteria and screening by at least two reviewers, 34 articles were included in the review. The findings underscores the significance of trust and safety as foundational elements for promoting secure and trustworthy human-machine cooperation. Confirm that almost 30% of the revised articles do not present a definition of trust, which can be problematic as this lack of conceptual clarity can undermine research efforts in addressing this problem from a central perspective. It highlights that the choice of domain and area of application should influence the choice of methods and approaches to fostering trust in HRI, as those choices can significantly affect user preferences and their perceptions and assessment of robot capabilities. Additionally, this lack of conceptual clarity can be a potential barrier to fostering trust in HRI and explains the sometimes contradictory findings or choice of methods and instruments used to investigate trust in robots and other autonomous systems in the literature.
In the rapidly evolving field of artificial intelligence (AI), traditional benchmarks can fall short in attempting to capture the nuanced capabilities of AI models. We focus on the case of physical world modeling and propose a novel approach to augment existing benchmarks with human-derived evaluation criteria, aiming to enhance the interpretability and applicability of model behaviors. Grounding our study in the Perception Test and OpenEQA benchmarks, we conducted in-depth interviews and large-scale surveys to identify key cognitive skills, such as Prioritization, Memorizing, Discerning, and Contextualizing, that are critical for both AI and human reasoning. Our findings reveal that participants perceive AI as lacking in interpretive and empathetic skills yet hold high expectations for AI performance. By integrating insights from our findings into benchmark design, we offer a framework for developing more human-aligned means of defining and measuring progress. This work underscores the importance of user-centered evaluation in AI development, providing actionable guidelines for researchers and practitioners aiming to align AI capabilities with human cognitive processes. Our approach both enhances current benchmarking practices and sets the stage for future advancements in AI model evaluation.
Community engagement processes form a critical foundation of democratic governance, yet frequently struggle with resource constraints, sensemaking challenges, and barriers to inclusive participation. These processes rely on constructive communication between public leaders and community organizations characterized by understanding, trust, respect, legitimacy, and agency. As artificial intelligence (AI) technologies become increasingly integrated into civic contexts, they offer promising capabilities to streamline resource-intensive workflows, reveal new insights in community feedback, translate complex information into accessible formats, and facilitate reflection across social divides. However, these same systems risk undermining democratic processes through accuracy issues, transparency gaps, bias amplification, and threats to human agency. In this paper, we examine how human-AI collaboration might address these risks and transform civic communication dynamics by identifying key communication pathways and proposing design considerations that maintain a high level of control over decision-making for both public leaders and communities while leveraging computer automation. By thoughtfully integrating AI to amplify human connection and understanding while safeguarding agency, community engagement processes can utilize AI to promote more constructive communication in democratic governance.
The rapid rise of AI-generated art has sparked debate about potential biases in how audiences perceive and evaluate such works. This study investigates how composer information and listener characteristics shape the perception of AI-generated music, adopting a mixed-method approach. Using a diverse set of stimuli across various genres from two AI music models, we examine effects of perceived authorship on liking and emotional responses, and explore how attitudes toward AI, personality traits, and music-related variables influence evaluations. We further assess the influence of perceived humanness and analyze open-ended responses to uncover listener criteria for judging AI-generated music. Attitudes toward AI proved to be the best predictor of both liking and emotional intensity of AI-generated music. This quantitative finding was complemented by qualitative themes from our thematic analysis, which identified ethical, cultural, and contextual considerations as important criteria in listeners' evaluations of AI-generated music. Our results offer a nuanced view of how people experience music created by AI tools and point to key factors and methodological considerations for future research on music perception in human-AI interaction.
Janik Kaden, Maximilian Hilger, Tim Schreiter
et al.
With robots increasingly integrating into human environments, understanding and predicting human motion is essential for safe and efficient interactions. Modern human motion and activity prediction approaches require high quality and quantity of data for training and evaluation, usually collected from motion capture systems, onboard or stationary sensors. Setting up these systems is challenging due to the intricate setup of hardware components, extensive calibration procedures, occlusions, and substantial costs. These constraints make deploying such systems in new and large environments difficult and limit their usability for in-the-wild measurements. In this paper we investigate the possibility to apply the novel Ultra-Wideband (UWB) localization technology as a scalable alternative for human motion capture in crowded and occlusion-prone environments. We include additional sensing modalities such as eye-tracking, onboard robot LiDAR and radar sensors, and record motion capture data as ground truth for evaluation and comparison. The environment imitates a museum setup, with up to four active participants navigating toward random goals in a natural way, and offers more than 130 minutes of multi-modal data. Our investigation provides a step toward scalable and accurate motion data collection beyond vision-based systems, laying a foundation for evaluating sensing modalities like UWB in larger and complex environments like warehouses, airports, or convention centers.
Manuela Azevedo Carvalho, Luciana Aparecida de Miranda
A imagem corporal é constituída pela concretude material visual, mas também pela representação construída e compartilhada no imaginário social coletivo, a partir da significação sociocultural historicamente atribuída aos significantes que a compõem, como a cor da pele e os elementos que denotam gênero, numa perspectiva binária. Este texto toma o corpo como objeto, para discutir como a imagem e o imaginário social coletivo, alimentados por elaborações e representações socioculturais acerca de imagens corporais visuais e conceituais, atravessam as relações sociais de travestis e mulheres trans e de homens cis negros. Nas análises, o conceito de “imagem de controle” (Collins, 2019) foi a ferramenta teórico-metodológica fundamental para compreender como são atribuídas, a suas imagens visuais corporais e a suas representações, significações socioculturais, que remetem esses sujeitos a posições subalternas e, por vezes, à abjeção, legando-os a estereótipos negativos que funcionam para estruturar discriminações nas interações nos mais diversos espaços de sociabilidades e posições subalternizadas na hierarquia das desigualdades sociais, dissimulando condições sociais construídas como naturais e imutáveis.
Ethnology. Social and cultural anthropology, Human settlements. Communities
Co-creation has become a buzzword in city development (Brandsen et al., 2016). In its purest form it includes a set of participatory methods involving a variety of local stakeholders. Living in social housing in Denmark and Finland, however, offers a longer-standing tradition of participation to influence matters concerning housing in the social housing stock, namely resident democracy. In Finland, resident democracy is based on the law on joint administration of tenement houses. However, it has been termed “quasi-democracy”, as it merely enables residents to organise voluntary work (Huotari et al., 2017). In Denmark, resident democracy in the social housing sector has offered more involvement for residents in neighbourhood development than is generally the norm. However, while co-creation as a means for neighbourhood development has been growing, resident democracy is threatened in the social housing sector. This paper sets out to analyse and discuss the role of resident democracy in Denmark and Finland. While some cross-country comparisons of social housing systems and provision exist, very little is known about how resident democracy plays out in social housing in different countries. This paper compares recent developments and challenges of resident democracy in two social housing sectors as a means of pointing to a paradox: while in general resident involvement through co-creation in neighbourhood development is increasing, it is stagnating (Finland) or decreasing (Denmark) in the social housing sector, particularly for the residents of deprived areas. The possibility to influence the environment one lives in is of crucial importance for residents’ well-being. Alongside concurrent trends of the changing role of social housing, the diminishment of resident democracy can undermine a prior feeling of having some control over housing circumstances amongst the residents of the sector, some of whom are citizens with the least opportunities in the housing market.
This article presents an implementation of a natural-language speech interface and a haptic feedback interface that enables a human supervisor to provide guidance to, request information, and receive status updates from a Spot robot. We provide insights gained during preliminary user testing of the interface in a realistic robot exploration scenario.
Generating lifelike 3D humans from a single RGB image remains a challenging task in computer vision, as it requires accurate modeling of geometry, high-quality texture, and plausible unseen parts. Existing methods typically use multi-view diffusion models for 3D generation, but they often face inconsistent view issues, which hinder high-quality 3D human generation. To address this, we propose Human-VDM, a novel method for generating 3D human from a single RGB image using Video Diffusion Models. Human-VDM provides temporally consistent views for 3D human generation using Gaussian Splatting. It consists of three modules: a view-consistent human video diffusion module, a video augmentation module, and a Gaussian Splatting module. First, a single image is fed into a human video diffusion module to generate a coherent human video. Next, the video augmentation module applies super-resolution and video interpolation to enhance the textures and geometric smoothness of the generated video. Finally, the 3D Human Gaussian Splatting module learns lifelike humans under the guidance of these high-resolution and view-consistent images. Experiments demonstrate that Human-VDM achieves high-quality 3D human from a single image, outperforming state-of-the-art methods in both generation quality and quantity. Project page: https://human-vdm.github.io/Human-VDM/
Eike Schneiders, Steve Benford, Alan Chamberlain
et al.
We reflect on the design of a multispecies world centred around a bespoke enclosure in which three cats and a robot arm coexist for six hours a day during a twelve-day installation as part of an artist-led project. In this paper, we present the project's design process, encompassing various interconnected components, including the cats, the robot and its autonomous systems, the custom end-effectors and robot attachments, the diverse roles of the humans-in-the-loop, and the custom-designed enclosure. Subsequently, we provide a detailed account of key moments during the deployment and discuss the design implications for future multispecies systems. Specifically, we argue that designing the technology and its interactions is not sufficient, but that it is equally important to consider the design of the `world' in which the technology operates. Finally, we highlight the necessity of human involvement in areas such as breakdown recovery, animal welfare, and their role as audience.
Martin Wolfgang Lauer-Schmaltz, Philip Cash, John Paulin Hansen
et al.
Human Digital Twins (HDTs) are a fast-emerging technology with significant potential in fields ranging from healthcare to sports. HDTs extend the traditional understanding of Digital Twins by representing humans as the underlying physical entity. This has introduced several significant challenges, including ambiguity in the definition of HDTs and a lack of guidance for their design. This survey brings together the recent advances in the field of HDTs to guide future developers by proposing a first cross-domain definition of HDTs based on their characteristics, as well as eleven key design considerations that emerge from the associated challenges.
Saliency maps can explain how deep neural networks classify images. But are they actually useful for humans? The present systematic review of 68 user studies found that while saliency maps can enhance human performance, null effects or even costs are quite common. To investigate what modulates these effects, the empirical outcomes were organised along several factors related to the human tasks, AI performance, XAI methods, images to be classified, human participants and comparison conditions. In image-focused tasks, benefits were less common than in AI-focused tasks, but the effects depended on the specific cognitive requirements. Moreover, benefits were usually restricted to incorrect AI predictions in AI-focused tasks but to correct ones in image-focused tasks. XAI-related factors had surprisingly little impact. The evidence was limited for image- and human-related factors and the effects were highly dependent on the comparison conditions. These findings may support the design of future user studies.
Dimas Pratidina Puriastuti Hadiani, Tri Ida Wahyu Kustyorini
Poultry concentrate feed is predominantly produced by animal feed factories in Indonesia. Unfortunately, the selling price of concentrates has been steadily increasing, causing difficulties for poultry farmers in maintaining their businesses. Indonesia, being an agricultural country, possesses a wealth of agricultural products. The abundance of agricultural products, coupled with significant amounts of agricultural waste from by-products, can be processed and utilized as animal feed. In order to address this issue, a service activity was conducted in Suru Village, Doko District, Blitar Regency. The objective was to provide socialization and training on utilizing agricultural waste, which is abundant in Suru Village, as an alternative feed for poultry livestock. The approach employed involved socializing the activities to farmers, addressing the challenges faced by partners, and providing counseling and direct assistance. The counseling sessions focused on the types of agricultural waste readily available in Suru Village that can be utilized in poultry feed, as well as training on formulating simple rations. As a result of this activity, breeders were able to utilize rice bran, an agricultural waste product, as an alternative feed, thereby reducing the cost of expensive commercial feeds. By substituting rice bran in feed formulations for commercial concentrates, farmers were able to save Rp410,000 per 100 kg of purchased feed.
Las zonas metropolitanas concentran población y atraen actividades económicas, induciendo la necesidad de realizar viajes dentro de su territorio. Por ello, la presente investigación analiza las características y distribución espacial de los viajes por motivos tanto obligados como opcionales en los principales medios de transporte, y se emplea la encuesta Origen Destino para analizar el modelo clásico de la movilidad. Los resultados muestran que el autobús es el principal medio para realizar desplazamientos por motivo educativos, mientras el automóvil es mayormente utilizado con propósitos de compras y de trabajo, además que la centralidad de la residencia se relaciona directamente con la del empleo.
Human settlements. Communities, Demography. Population. Vital events
This paper examines the recent advances and applications of AI in human geography especially the use of machine (deep) learning, including place representation and modeling, spatial analysis and predictive mapping, and urban planning and design. AI technologies have enabled deeper insights into complex human-environment interactions, contributing to more effective scientific exploration, understanding of social dynamics, and spatial decision-making. Furthermore, human geography offers crucial contributions to AI, particularly in context-aware model development, human-centered design, biases and ethical considerations, and data privacy. The synergy beween AI and human geography is essential for addressing global challenges like disaster resilience, poverty, and equitable resource access. This interdisciplinary collaboration between AI and geography will help advance the development of GeoAI and promise a better and sustainable world for all.
Handovers are basic yet sophisticated motor tasks performed seamlessly by humans. They are among the most common activities in our daily lives and social environments. This makes mastering the art of handovers critical for a social and collaborative robot. In this work, we present an experimental study that involved human-human handovers by 13 pairs, i.e., 26 participants. We record and explore multiple features of handovers amongst humans aimed at inspiring handovers amongst humans and robots. With this work, we further create and publish a novel data set of 8672 handovers, bringing together human motion and the forces involved. We further analyze the effect of object weight and the role of visual sensory input in human-human handovers, as well as possible design implications for robots. As a proof of concept, the data set was used for creating a human-inspired data-driven strategy for robotic grip release in handovers, which was demonstrated to result in better robot to human handovers.