As robots increasingly become part of shared human spaces, their movements must transcend basic functionality by incorporating expressive qualities to enhance engagement and communication. This paper introduces a movement-centered design pedagogy designed to support engineers in creating expressive robotic arm movements. Through a hands-on interactive workshop informed by interdisciplinary methodologies, participants explored various creative possibilities, generating valuable insights into expressive motion design. The iterative approach proposed integrates analytical frameworks from dance, enabling designers to examine motion through dynamic and embodied dimensions. A custom manual remote controller facilitates interactive, real-time manipulation of the robotic arm, while dedicated animation software supports visualization, detailed motion sequencing, and precise parameter control. Qualitative analysis of this interactive design process reveals that the proposed "toolbox" effectively bridges the gap between human intent and robotic expressiveness resulting in more intuitive and engaging expressive robotic arm movements.

The drone industry is diversifying and the number of pilots increasing rapidly. In this context, flight schools need adapted tools to train pilots, most importantly with regard to their own awareness of their physiological and cognitive limits. In civil and military aviation, pilots can train on realistic simulators to tune their reaction and reflexes, but also to gather data on their piloting behavior and physiological states, helping to improve their performance. As opposed to cockpit scenarios, drone teleoperation is conducted outdoors in the field, with only limited potential from desktop simulation training. This work aims to provide a solution to gather pilot behavior in the field and help them increase their performance. We combined advanced object detection from a frontal camera with gaze and heart rate variability measurements. We observed pilots and analyzed their behavior over three flight challenges. We believe this tool can support pilots both in their training and in their regular flight tasks.

This paper provides an overview of the latest trends in robotics research and development, with a particular focus on applications in manufacturing and industrial settings. We highlight recent advances in robot design, including cutting-edge collaborative robot mechanics and advanced safety features, as well as exciting developments in perception and human-swarm interaction. By examining recent contributions from Kinova, a leading robotics company, we illustrate the differences between industry and academia in their approaches to developing innovative robotic systems and technologies that enhance productivity and safety in the workplace. Ultimately, this paper demonstrates the tremendous potential of robotics to revolutionize manufacturing and industrial operations, and underscores the crucial role of companies like Kinova in driving this transformation forward.

The ability to automatically infer relevant aspects of human users’ thoughts and feelings is crucial for technologies to adapt their behaviors in complex interactions intelligently (e.g., social robots or tutoring systems). Research on multimodal analysis has demonstrated the potential of technology to provide such estimates for a broad range of internal states and processes. However, constructing robust enough approaches for deployment in real-world applications remains an open problem. The MSECP-Wild workshop series serves as a multidisciplinary forum to present and discuss research addressing this challenge. This 4th iteration focuses on addressing varying contextual conditions (e.g., throughout an interaction or across different situations and environments) in intelligent systems as a crucial barrier for more valid real-world predictions and actions. Submissions to the workshop span efforts relevant to multimodal data collection and context-sensitive modeling. These works provide important impulses for discussions of the state-of-the-art and opportunities for future research on these subjects.

Rolling spherical robots have been studied in the past few years as an alternative to legged and wheeled robots in unstructured environments. These systems are of uttermost interest for space exploration: fast, robust to collision and able to handle various terrain topologies. This paper introduces a novel barycentric spherical robot, dubbed the Autonomous Robotic Intelligent Explorer Sphere (ARIES). Equipped with an actuated cylindrical joint acting as a pendulum with two degrees-of-freedom (DoF), the ARIES has a continuous differential transmission to allow simultaneous rolling and steering. This mechanism allows an unprecedented mass allocation optimization, notably to provide a low center of mass. Kinematics and dynamics of this novel system are detailed. An analysis of the steering mechanism proves that it is more efficient than a more conventional 2-DoF tilting mechanism, while also retaining more space for a payload, for instance to host sensors for simultaneous localization and mapping, in the upper part of the sphere. Moreover, the kinematic input/output equations obtained significantly simplify the device's control. Finally, we present a first complete prototype with preliminary experimental tests.

The use of multi-robot systems is increasing in disaster response, industry, transport, and logistics. Humans will remain indispensable to control and manage these fleets of robots, particularly in safety-critical applications. However, a human operator’s cognitive capacities can be challenged and exceeded as the sizes of autonomous fleets grow, and more sophisticated AI techniques can lead to opaque robot control programs. In a user study (n = 40), we explore how autonomous swarm intelligence algorithms and novel tangible interaction modalities relate to subjective and physiological indices of operator cognitive load (i.e., NASA Task Load Index, heart rate variability). Our findings suggest that there are differences in workload across conditions; however, subjective and cardiac measures appear to be sensitive to different aspects of cognitive state. The results hint at the potential of both tangible interfaces and automation to engage operators and reduce cognitive load, yet show the need for further validation of workload measures for use in studying and optimizing human-swarm interactions.

Behaviours of robot swarms often take inspiration from biological models, such as ant colonies and bee hives. Yet, understanding how these behaviours are actually perceived by human users has so far received limited attention. In this paper, we use animations to represent different kinds of possible swarm motions intended to communicate specific messages to a human. We explore how these animations relate to the perceived group cohesiveness of the swarm, comprised of five different parameters: synchronising, grouping, following, reacting, and shape forming. We conducted an online user study where 98 participants viewed nine animations of a swarm displaying different behaviours and rated them for perceived group cohesiveness. We found that the parameters of group cohesiveness correlated with the messages the swarm was perceived as communicating. In particular, the message of initiating communication was highly positively correlated with all group parameters, whereas broken communication was negatively correlated. In addition, the importance of specific group parameters differed within each animation. For example, the parameter of grouping was most associated with animations signalling an intervention is needed. These findings are discussed within the context of designing intuitive behaviour for robot swarms.

Modeling with multimodal data in the wild poses similar challenges in human-computer and human-robot interaction (HCI, HRI). This workshop series thus blends HCI and HRI to jointly address a broad range of current topics in multimodal modeling aimed at designing intelligent systems in the wild. From addressing data scarcity in multimodal user state recognition to emotion prediction from EEG while listening to music, our third workshop in this series aims to further stimulate this important multidisciplinary exchange.

Many applications related to the Internet of Things, such as tracking people or objects, robotics, and monitoring require the localization of large networks of devices in dynamic, GPS-denied environments. Ultrawideband (UWB) technology is a common choice because of its precise ranging capability. However, allowing access and effective use of the shared UWB medium with a constantly changing set of devices faces some particular challenges: high frequency of ranging measurements by the devices to improve system accuracy; network topology changes requiring rapid adaptation; and decentralized operation to avoid single points of failure. In this article, we propose a novel time-division multiple access (TDMA) algorithm that can quickly schedule the use of the UWB medium by a large network of devices without collisions in local network neighborhoods and avoiding conflicts with hidden terminals, all the while maximizing network usage. Using exclusively the UWB radio network, we realize a decentralized system for synchronization, dynamic TDMA scheduling, and precise relative positioning on a multihop network. Our system does not have special nodes (all nodes are equal) and it is sufficiently scalable for real-world applications. Our method can be applied to implement device localization services in large spaces without GPS and complex topologies, such as malls, museums, mines, etc. We demonstrate our method in simulation and on real hardware in an underground parking lot, showing the effectiveness of its TDMA schedule for relative localization.

Small unmanned aerial vehicles (UAVs) have generally little capacity to carry payloads. Through collaboration, the UAVs can increase their joint payload capacity and carry more significant loads. For maximum flexibility to dynamic and unstructured environments and task demands, we propose a fully decentralized control infrastructure based on a swarm-specific scripting language, Buzz. In this paper, we describe the control infrastructure and use it to compare two algorithms for collaborative transport: field potentials and spring-damper. We test the performance of our approach with a fleet of micro-UAVs, demonstrating the potential of decentralized control for collaborative transport.

The control of multiple robots in the context of tele-exploration tasks is often attentionally taxing, resulting in a loss of situational awareness for operators. Unmanned aerial vehicle swarms require significantly more multitasking than controlling a plane, thus making it necessary to devise intuitive feedback sources and control methods for these robots. The purpose of this article is to examine a swarm's nonverbal behaviour as a possible way to increase situational awareness and reduce the operators cognitive load by soliciting intuitions about the swarm's behaviour. To progress on the definition of a database of nonverbal expressions for robot swarms, we first define categories of communicative intents based on spontaneous descriptions of common swarm behaviours. The obtained typology confirms that the first two levels (as defined by Endsley: elements of environment and comprehension of the situation) can be shared through swarms motion-based communication. We then investigate group motion parameters potentially connected to these communicative intents. Results are that synchronized movement and tendency to form figures help convey meaningful information to the operator. We then discuss how this can be applied to realistic scenarios for the intuitive command of remote robotic teams.

In recent years, researchers have explored human body posture and motion to control robots in more natural ways. These interfaces require the ability to track the body movements of the user in three dimensions. Deploying motion capture systems for tracking tends to be costly and intrusive and requires a clear line of sight, making them ill adapted for applications that need fast deployment. In this article, we use consumer-grade armbands, capturing orientation information and muscle activity, to interact with a robotic system through a state machine controlled by a body motion classifier. To compensate for the low quality of the information of these sensors, and to allow a wider range of dynamic control, our approach relies on machine learning. We train our classifier directly on the user to recognize (within minutes) which physiological state his or her body motion expresses. We demonstrate that on top of guaranteeing faster field deployment, our algorithm performs better than all comparable algorithms, and we detail its configuration and the most significant features extracted. As the use of large groups of robots is growing, we postulate that their interaction with humans can be eased by our approach. We identified the key factors to stimulate engagement using our system on 27 participants, each creating his or her own set of expressive motions to control a swarm of desk robots. The resulting unique dataset is available online together with the classifier and the robot control scripts.

One of the safest and most reliable strategies for vehicle's collision avoidance is embedded control at low level to guarantee safe motion in all situations using on-board sensors. In this paper, we propose a novel lightweight collision avoidance strategy that can be implemented as a low level motion control to achieve safe motion while simultaneously tracking the robot's reference control input. This strategy is designed to be general so that it can be easily integrated with most control designs, with the primary target of resource-constrained robot swarms that act in real-time, dynamic environments. The main advantages of our approach are a very simple structure and low computational requirements. We verified the effectiveness of the proposed collision avoidance strategy through two simulated scenarios and with physical robots. We believe our design can be directly used in many areas, such as autonomous driving, intelligent transportation and planetary exploration.

In this paper, we demonstrate how behavioural structure, such as a finite state machine, can emerge in minimal robots without computation nor memory capabilities. As a case study we observe the ability of a group of non-holonomic robots to form robust, self-healing circle formations in a decentralized manner using only a limited frontal binary sensor. We present a grid-search method to find suitable parameters that promote the formation of a stable circle. We then examine how the parameters of the controllers affect the appearance of the behaviour, and provide theoretical proof for its emergence and self-healing properties. We validate the proposed model through a set of experiments with ten mobile real robots. Our results with real robots match the simulated experiments and provide insights on how a simple, computation-free behaviour can generate complex spatio-temporal dynamics.

The robotics community is considering the use of large groups of robots, also known as artificial swarms for applications in unknown and dynamic environments. In this context, swarms of robot will need to interact with users to accomplish their mission. Unfortunately, little is known about the users' perception of group behavior and dynamics, as well as what is the best interaction modality for swarms. In this paper, we focus on the movement of the swarm as a group to convey information to a user: we believe that the interpretation of artificial states based solely on the motion can lead to promising natural interaction modalities. We define the expressivity of a movement as a metric to understand how natural, readable, or easily understandable such movement may appear. We then correlate expressivity with the control parameters for the distributed behaviour of the swarm. A user study confirms the relationship between inter-robot distance, temporal and spatial synchronicity, and the perceived expressivity of the robotic system.

In recent years, gesture-based interfaces have been explored in order to control robots in non-traditional ways. These require the use of systems that are able to track human body movements in 3D space. Deploying Mo-cap or camera systems to perform this tracking tend to be costly, intrusive, or require a clear line of sight, making them ill-adapted for artistic performances. In this paper, we explore the use of consumer-grade armbands (Myo armband) which capture orientation information (via an inertial measurement unit) and muscle activity (via electromyography) to ultimately guide a robotic device during live performances. To compensate for the drop in information quality, our approach rely heavily on machine learning and leverage the multimodality of the sensors. In order to speed-up classification, dimensionality reduction was performed automatically via a method based on Random Forests (RF). Online classification results achieved 88% accuracy over nine movements created by a dancer during a live performance, demonstrating the viability of our approach. The nine movements are then grouped into three semantically-meaningful moods by the dancer for the purpose of an artistic performance achieving 94% accuracy in real-time. We believe that our technique opens the door to aesthetically-pleasing sequences of body motions as gestural interface, instead of traditional static arm poses.

On October 26th 2010, a unique HRI-artistic public experiment took place at the UsineC theater, in Montreal. It was the result of a many-months collaboration between the Montreal based lab hosting the [ VOILES | SAILS ] research-creation platform (Self-Assembling Intelligent Ligther-than-air Structures) and the well-known Australian artist Stelarc and his team, who work on artificial agents' embodiment and robotic behaviour modeling.