Swarm fragmentation, the breakdown of communication and coordination among robots, can critically compromise a swarm's mission. Integrating Augmented Reality support into swarm monitoring-especially through co-located visualisations anchored directly on the robots- may enable human operators to detect early signs of fragmentation and intervene effectively. In this work, we propose three localised visual cues-targeting robot connectivity, dominant decision influences, and movement direction-to make explicit the underlying Perception-Decision-Action (PDA) loop of each robot. Through an immersive Virtual Reality user study, 51 participants were tasked with both anticipating potential fragmentation and selecting the appropriate control to prevent it, while observing swarms exhibiting expansion, densification, flocking, and swarming behaviours. Our results reveal that a visualisation emphasising inter-robot connectivity significantly improves anticipation of fragmentation, though none of the cues consistently enhance control selection over a baseline condition. These findings underscore the potential of co-located AR-enhanced visual feedback to support human-swarm interaction and inform the design of future AR-based supervisory systems for robot swarms. A free copy of this paper and all supplemental materials are available at https://osf.io/49gny.

We address the problem of learning imitation policies that generalize across environments sharing the same underlying causal structure between the system dynamics and the task.We introduce a novel loss for learning invariant state representations that draws inspiration from adversarial robustness.Our approach is algorithm-agnostic and does not require knowledge of domain labels.Yet, evaluation in visual and non-visual environments reveals improved zero-shot generalization in the presence of spurious features compared to previous works.

In the context of robot swarms, fragmentation refers to a breakdown in communication and coordination among the robots. This fragmentation can lead to issues in the swarm self-organisation, especially the loss of efficiency or an inability to perform their tasks. Human operators influencing the swarm could prevent fragmentation. To help them in this task, it is necessary to study the ability of humans to perceive and anticipate fragmentation. This article studies the perception of different types of fragmentation occurring in swarms depending on their behaviour selected amongst swarming, flocking, expansion and densification. Thus, we characterise human perception thanks to two metrics based on the distance separating fragmented groups and the separation speed. The experimentation protocol consists of a binary discrimination task in which participants have to assess the presence of fragmentation. The results show that detecting fragmentation for expansion behaviour and anticipating fragmentation, in general, are challenging. Moreover, they show that humans rely on separation distance and speed to infer the presence or absence of fragmentation. Our study paves the way for new research that will provide information to humans to better anticipate and efficiently prevent the occurrence of swarm fragmentation.

International audience

In this paper we present HeloVis: a 3D interactive visualization that relies on immersive properties to improve the user performance during SIGINT analysis. SIGINT, which stands for SIGnal INTelligence, is a field facing many challenges like huge amounts of data, complex data and novice users. HeloVis draws on perceptive biases, highlighted by Gestalt laws, and on depth perception to enhance the recurrence properties contained into the data and to abstract from interferences such as noise or missing data. In this paper, we first present SIGINT and the challenges that it brings to visual analytics. Then, we present the existing work that is currently used in or that fits the SIGINT context. Finally, we present HeloVis, an innovative application on an immersive context that allows performing SIGINT analysis and we present its evaluation performed with military operators who are the end-users of SIGINT analysis.

In this paper we address the question of the relationships between visualization challenges and the representation components that provide solutions to these challenges. Our approach involves extracting such relationships through an identification of the context and the components of a significant number of representations and a comparison of the result to existing theoretical studies. To make such an identification possible, we rely on a characterization of the representation context based on a thoughtful aggregation of existing characterizations of the data type, the tasks and the context of use of the representations. We illustrate our approach on a use-case with examples of a relationships extraction and of a comparison of that relationships to the theory. We believe that the establishment of such relationships makes it possible to understand the mechanisms behind the representations, in order to build a representation design recommendation tool. Such a tool will enable us to recommend the components to use in a representation, given a visualization challenge to address.

The focus of visual attention has been argued to play a key role in object recognition. Many computational models of visual attention were proposed to estimate locations of eye fixations driven by bottom-up stimuli. Most of these models rely on pyramids consisting of multiple scaled versions of the visual scene. This design aims at capturing the fact that neural cells in higher visual areas tend to have larger receptive fields (RFs). On the other hand, very few models represent multi-scaling resulting from the eccentricity-dependent RF sizes within each visual layer, also known as the cortical magnification effect. In this paper, we demonstrate that using a cortical-magnification-like mechanism can lead to performant alternatives to pyramidal approaches in the context of attentional modeling. Moreover, we argue that introducing such a mechanism equips the proposed model with additional properties related to overt attention and distance-dependent saliency that are worth exploring.

Interest in the area of collaborative Unmanned Aerial Vehicles (UAVs) in a Multi-Agent System is growing to compliment the strengths and weaknesses of the human-machine relationship. To achieve effective management of multiple heterogeneous UAVs, the status model of the agents must be communicated to each other. This paper presents the effects on operator Cognitive Workload (CW), Situation Awareness (SA), trust and performance by increasing the autonomy capability transparency through text-based communication of the UAVs to the human agents. The results revealed a reduction in CW, increase in SA, increase in the Competence, Predictability and Reliability dimensions of trust, and the operator performance.

This paper proposes a two-part contribution to the human factor issues raised by the prospect of the deployment of swarms of autonomous robots under the supervision of a human operator. First, we propose a generic framework for system description that allows us to analyze and guide the design of automated systems. Second, we analyze the way some highly adaptive systems such as swarms can be perceived by operators, and especially the way representational gaps between man and machine can impact global performances.