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Robotics

Explore arXiv robotics papers and cs.RO metadata. Search for robot learning, motion planning and control, then check experiments in the source manuscript.

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arXiv2023

Can Robotic Experimenters help improve HRI Experiments? An Experimental Study

To evaluate the design and skills of a robot or an algorithm for robotics, human-robot interaction user studies need to be performed. Classically, these studies are conducted by human experimenters, requiring considerable effort, and introducing variability and potential human error. In this paper, we investigate the use of robots in support of HRI experiments. Robots can perform repeated tasks accurately, thereby reducing human effort and improving validity through reduction of error and variability between participants. To assess the potential for robot led HRI experiments, we ran an HRI experiment with two participant groups, one led by a human experimenter and another led mostly by a robot experimenter.We show that the replacement of several repetitive experiment tasks through robots is not only possible but beneficial: Trials performed by the robot experimenter had fewer errors and were more fluent. There was no statistically significant difference in participants' perception w.r.t. cognitive load, comfortability, enjoyment, safety, trust and understandability between both groups. To the best of our knowledge, this is the first comparison between robot-led and human-led HRI experiments. It suggests that using robot experimenters can be beneficial and should be considered.

cs.RO↗

arXiv2024

Self-organizing Nervous Systems for Robot Swarms

The system architecture controlling a group of robots is generally set before deployment and can be either centralized or decentralized. This dichotomy is highly constraining, because decentralized systems are typically fully self-organized and therefore difficult to design analytically, whereas centralized systems have single points of failure and limited scalability. To address this dichotomy, we present the Self-organizing Nervous System (SoNS), a novel robot swarm architecture based on self-organized hierarchy. The SoNS approach enables robots to autonomously establish, maintain, and reconfigure dynamic multi-level system architectures. For example, a robot swarm consisting of $n$ independent robots could transform into a single $n$-robot SoNS and then into several independent smaller SoNSs, where each SoNS uses a temporary and dynamic hierarchy. Leveraging the SoNS approach, we show that sensing, actuation, and decision-making can be coordinated in a locally centralized way, without sacrificing the benefits of scalability, flexibility, and fault tolerance, for which swarm robotics is usually studied. In several proof-of-concept robot missions -- including binary decision-making and search-and-rescue -- we demonstrate that the capabilities of the SoNS approach greatly advance the state of the art in swarm robotics. The missions are conducted with a real heterogeneous aerial-ground robot swarm, using a custom-developed quadrotor platform. We also demonstrate the scalability of the SoNS approach in swarms of up to 250 robots in a physics-based simulator, and demonstrate several types of system fault tolerance in simulation and reality.

cs.RO↗

arXiv2024

Human-mediated Large Language Models for Robotic Intervention in Children with Autism Spectrum Disorders

The robotic intervention for individuals with Autism Spectrum Disorder (ASD) has generally used pre-defined scripts to deliver verbal content during one-to-one therapy sessions. This practice restricts the use of robots to limited, pre-mediated instructional curricula. In this paper, we increase robot autonomy in one such robotic intervention for children with ASD by implementing perspective-taking teaching. Our approach uses large language models (LLM) to generate verbal content as texts and then deliver it to the child via robotic speech. In the proposed pipeline, we teach perspective-taking through which our robot takes up three roles: initiator, prompter, and reinforcer. We adopted the GPT-2 + BART pipelines to generate social situations, ask questions (as initiator), and give options (as prompter) when required. The robot encourages the child by giving positive reinforcement for correct answers (as a reinforcer). In addition to our technical contribution, we conducted ten-minute sessions with domain experts simulating an actual perspective teaching session, with the researcher acting as a child participant. These sessions validated our robotic intervention pipeline through surveys, including those from NASA TLX and GodSpeed. We used BERTScore to compare our GPT-2 + BART pipeline with an all GPT-2 and found the performance of the former to be better. Based on the responses by the domain experts, the robot session demonstrated higher performance with no additional increase in mental or physical demand, temporal demand, effort, or frustration compared to a no-robot session. We also concluded that the domain experts perceived the robot as ideally safe, likable, and reliable.

cs.RO↗

arXiv2024

EVE: Enabling Anyone to Train Robots using Augmented Reality

The increasing affordability of robot hardware is accelerating the integration of robots into everyday activities. However, training a robot to automate a task requires expensive trajectory data where a trained human annotator moves a physical robot to train it. Consequently, only those with access to robots produce demonstrations to train robots. In this work, we remove this restriction with EVE, an iOS app that enables everyday users to train robots using intuitive augmented reality visualizations, without needing a physical robot. With EVE, users can collect demonstrations by specifying waypoints with their hands, visually inspecting the environment for obstacles, modifying existing waypoints, and verifying collected trajectories. In a user study (N=14, D=30) consisting of three common tabletop tasks, EVE outperformed three state-of-the-art interfaces in success rate and was comparable to kinesthetic teaching-physically moving a physical robot-in completion time, usability, motion intent communication, enjoyment, and preference (mean of p=0.30). EVE allows users to train robots for personalized tasks, such as sorting desk supplies, organizing ingredients, or setting up board games. We conclude by enumerating limitations and design considerations for future AR-based demonstration collection systems for robotics.

cs.HC↗

arXiv2025

A Longitudinal Study of Child Wellbeing Assessment via Online Interactions with a Social Robot

Socially Assistive Robots are studied in different Child-Robot Interaction settings. However, logistical constraints limit accessibility, particularly affecting timely support for mental wellbeing. In this work, we have investigated whether online interactions with a robot can be used for the assessment of mental wellbeing in children. The children (N=40, 20 girls and 20 boys; 8-13 years) interacted with the Nao robot (30-45 mins) over three sessions, at least a week apart. Audio-visual recordings were collected throughout the sessions that concluded with the children answering user perception questionnaires pertaining to their anxiety towards the robot, and the robot's abilities. We divided the participants into three wellbeing clusters (low, med and high tertiles) using their responses to the Short Moods and Feelings Questionnaire (SMFQ) and further analysed how their wellbeing and their perceptions of the robot changed over the wellbeing tertiles, across sessions and across participants' gender. Our primary findings suggest that (I) online mediated-interactions with robots can be effective in assessing children's mental wellbeing over time, and (II) children's overall perception of the robot either improved or remained consistent across time. Supplementary exploratory analyses have also revealed that the gender of the children affected their wellbeing assessments with interactions effectively distinguishing between varying levels of wellbeing for both boys and girls for the first session and only for boys during the second session. The analyses have also revealed that girls have a higher opinion of the robot as a confidante as compared with boys. Findings from this work affirm the potential of using online mediated interactions with robots for the assessment of the mental wellbeing of children.

cs.HC↗

arXiv2024

The Influence of Demographic Variation on the Perception of Industrial Robot Movements

The influence of individual differences on the perception and evaluation of interactions with robots has been researched for decades. Some human demographic characteristics have been shown to affect how individuals perceive interactions with robots. Still, it is to-date not clear whether, which and to what extent individual differences influence how we perceive robots, and even less is known about human factors and their effect on the perception of robot movements. In addition, most results on the relevance of individual differences investigate human-robot interactions with humanoid or social robots whereas interactions with industrial robots are underrepresented. We present a literature review on the relationship of robot movements and the influence of demographic variation. Our review reveals a limited comparability of existing findings due to a lack of standardized robot manipulations, various dependent variables used and differing experimental setups including different robot types. In addition, most studies have insufficient sample sizes to derive generalizable results. To overcome these shortcomings, we report the results from a Web-based experiment with 930 participants that studies the effect of demographic characteristics on the evaluation of movement behaviors of an articulated robot arm. Our findings demonstrate that most participants prefer an approach from the side, a large movement range, conventional numbers of rotations, smooth movements and neither fast nor slow movement speeds. Regarding individual differences, most of these preferences are robust to demographic variation, and only gender and age was found to cause slight preference differences between slow and fast movements.

cs.RO↗

arXiv2025

Adaptive Electronic Skin Sensitivity for Safe Human-Robot Interaction

Artificial electronic skins covering complete robot bodies can make physical human-robot collaboration safe and hence possible. Standards for collaborative robots (e.g., ISO/TS 15066) prescribe permissible forces and pressures during contacts with the human body. These characteristics of the collision depend on the speed of the colliding robot link but also on its effective mass. Thus, to warrant contacts complying with the Power and Force Limiting (PFL) collaborative regime but at the same time maximizing productivity, protective skin thresholds should be set individually for different parts of the robot bodies and dynamically on the run. Here we present and empirically evaluate four scenarios: (a) static and uniform - fixed thresholds for the whole skin, (b) static but different settings for robot body parts, (c) dynamically set based on every link velocity, (d) dynamically set based on effective mass of every robot link. We perform experiments in simulation and on a real 6-axis collaborative robot arm (UR10e) completely covered with sensitive skin (AIRSKIN) comprising eleven individual pads. On a mock pick-and-place scenario with transient collisions with the robot body parts and two collision reactions (stop and avoid), we demonstrate the boost in productivity in going from the most conservative setting of the skin thresholds (a) to the most adaptive setting (d). The threshold settings for every skin pad are adapted with a frequency of 25 Hz. This work can be easily extended for platforms with more degrees of freedom and larger skin coverage (humanoids) and to social human-robot interaction scenarios where contacts with the robot will be used for communication.

cs.RO↗

arXiv2024

Development and Testing of a Vine Robot for Urban Search and Rescue in Confined Rubble Environments

The request for fast response and safe operation after natural and man-made disasters in urban environments has spurred the development of robotic systems designed to assist in search and rescue operations within complex rubble sites. Traditional Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) face significant limitations in such confined and obstructed environments. This paper introduces a novel vine robot designed to navigate dense rubble, drawing inspiration from natural growth mechanisms found in plants. Unlike conventional robots, vine robots are soft robots that can grow by everting their material, allowing them to navigate through narrow spaces and obstacles. The prototype presented in this study incorporates pneumatic muscles for steering and oscillation, an equation-based robot length control plus feedback pressure regulating system for extending and retracting the robot body. We conducted a series of controlled experiments in an artificial rubble testbed to assess the robot performance under varying environmental conditions and robot parameters, including volume ratio, environmental weight, oscillation, and steering. The results show that the vine robot can achieve significant penetration depths in cluttered environments with mixed obstacle sizes and weights, and can maintain repeated trajectories, demonstrating potential for mapping and navigating complex underground paths. Our findings highlight the suitability of the vine robot for urban search and rescue missions, with further research planned to enhance its robustness and deployability in real-world scenarios.

cs.RO↗

arXiv2025

Enabling Novel Mission Operations and Interactions with ROSA: The Robot Operating System Agent

The advancement of robotic systems has revolutionized numerous industries, yet their operation often demands specialized technical knowledge, limiting accessibility for non-expert users. This paper introduces ROSA (Robot Operating System Agent), an AI-powered agent that bridges the gap between the Robot Operating System (ROS) and natural language interfaces. By leveraging state-of-the-art language models and integrating open-source frameworks, ROSA enables operators to interact with robots using natural language, translating commands into actions and interfacing with ROS through well-defined tools. ROSA's design is modular and extensible, offering seamless integration with both ROS1 and ROS2, along with safety mechanisms like parameter validation and constraint enforcement to ensure secure, reliable operations. While ROSA is originally designed for ROS, it can be extended to work with other robotics middle-wares to maximize compatibility across missions. ROSA enhances human-robot interaction by democratizing access to complex robotic systems, empowering users of all expertise levels with multi-modal capabilities such as speech integration and visual perception. Ethical considerations are thoroughly addressed, guided by foundational principles like Asimov's Three Laws of Robotics, ensuring that AI integration promotes safety, transparency, privacy, and accountability. By making robotic technology more user-friendly and accessible, ROSA not only improves operational efficiency but also sets a new standard for responsible AI use in robotics and potentially future mission operations. This paper introduces ROSA's architecture and showcases initial mock-up operations in JPL's Mars Yard, a laboratory, and a simulation using three different robots. The core ROSA library is available as open-source.

cs.RO↗

arXiv2024

LLM2Swarm: Robot Swarms that Responsively Reason, Plan, and Collaborate through LLMs

Robot swarms are composed of many simple robots that communicate and collaborate to fulfill complex tasks. Robot controllers usually need to be specified by experts on a case-by-case basis via programming code. This process is time-consuming, prone to errors, and unable to take into account all situations that may be encountered during deployment. On the other hand, recent Large Language Models (LLMs) have demonstrated reasoning and planning capabilities, introduced new ways to interact with and program machines, and incorporate both domain-specific and commonsense knowledge. Hence, we propose to address the aforementioned challenges by integrating LLMs with robot swarms and show the potential in proofs of concept (showcases). For this integration, we explore two approaches. The first approach is 'indirect integration,' where LLMs are used to synthesize and validate the robot controllers. This approach may reduce development time and human error before deployment. Moreover, during deployment, it could be used for on-the-fly creation of new robot behaviors. The second approach is 'direct integration,' where each robot locally executes a separate LLM instance during deployment for robot-robot collaboration and human-swarm interaction. These local LLM instances enable each robot to reason, plan, and collaborate using natural language, as demonstrated in our showcases where the robots are able to detect a variety of anomalies, without prior information about the nature of these anomalies. To enable further research on our mainly conceptual contribution, we release the software and videos for our LLM2Swarm system: https://github.com/Pold87/LLM2Swarm.

cs.RO↗

arXiv2025

EMOS: Embodiment-aware Heterogeneous Multi-robot Operating System with LLM Agents

Heterogeneous multi-robot systems (HMRS) have emerged as a powerful approach for tackling complex tasks that single robots cannot manage alone. Current large-language-model-based multi-agent systems (LLM-based MAS) have shown success in areas like software development and operating systems, but applying these systems to robot control presents unique challenges. In particular, the capabilities of each agent in a multi-robot system are inherently tied to the physical composition of the robots, rather than predefined roles. To address this issue, we introduce a novel multi-agent framework designed to enable effective collaboration among heterogeneous robots with varying embodiments and capabilities, along with a new benchmark named Habitat-MAS. One of our key designs is $\textit{Robot Resume}$: Instead of adopting human-designed role play, we propose a self-prompted approach, where agents comprehend robot URDF files and call robot kinematics tools to generate descriptions of their physics capabilities to guide their behavior in task planning and action execution. The Habitat-MAS benchmark is designed to assess how a multi-agent framework handles tasks that require embodiment-aware reasoning, which includes 1) manipulation, 2) perception, 3) navigation, and 4) comprehensive multi-floor object rearrangement. The experimental results indicate that the robot's resume and the hierarchical design of our multi-agent system are essential for the effective operation of the heterogeneous multi-robot system within this intricate problem context.

cs.RO↗

arXiv2025

The Beatbots: A Musician-Informed Multi-Robot Percussion Quartet

Artistic creation is often seen as a uniquely human endeavor, yet robots bring distinct advantages to music-making, such as precise tempo control, unpredictable rhythmic complexities, and the ability to coordinate intricate human and robot performances. While many robotic music systems aim to mimic human musicianship, our work emphasizes the unique strengths of robots, resulting in a novel multi-robot performance instrument called the Beatbots, capable of producing music that is challenging for humans to replicate using current methods. The Beatbots were designed using an ``informed prototyping'' process, incorporating feedback from three musicians throughout development. We evaluated the Beatbots through a live public performance, surveying participants (N=28) to understand how they perceived and interacted with the robotic performance. Results show that participants valued the playfulness of the experience, the aesthetics of the robot system, and the unconventional robot-generated music. Expert musicians and non-expert roboticists demonstrated especially positive mindset shifts during the performance, although participants across all demographics had favorable responses. We propose design principles to guide the development of future robotic music systems and identify key robotic music affordances that our musician consultants considered particularly important for robotic music performance.

cs.RO↗

arXiv2025

Realm: Real-Time Line-of-Sight Maintenance in Multi-Robot Navigation with Unknown Obstacles

Multi-robot navigation in complex environments relies on inter-robot communication and mutual observations for coordination and situational awareness. This paper studies the multi-robot navigation problem in unknown environments with line-of-sight (LoS) connectivity constraints. While previous works are limited to known environment models to derive the LoS constraints, this paper eliminates such requirements by directly formulating the LoS constraints between robots from their real-time point cloud measurements, leveraging point cloud visibility analysis techniques. We propose a novel LoS-distance metric to quantify both the urgency and sensitivity of losing LoS between robots considering potential robot movements. Moreover, to address the imbalanced urgency of losing LoS between two robots, we design a fusion function to capture the overall urgency while generating gradients that facilitate robots' collaborative movement to maintain LoS. The LoS constraints are encoded into a potential function that preserves the positivity of the Fiedler eigenvalue of the robots' network graph to ensure connectivity. Finally, we establish a LoS-constrained exploration framework that integrates the proposed connectivity controller. We showcase its applications in multi-robot exploration in complex unknown environments, where robots can always maintain the LoS connectivity through distributed sensing and communication, while collaboratively mapping the unknown environment. The implementations are open-sourced at https://github.com/bairuofei/LoS_constrained_navigation.

cs.RO↗

arXiv2026

PeRoI: A Pedestrian-Robot Interaction Dataset for Learning Avoidance, Neutrality, and Attraction Behaviors in Social Navigation

Robots are increasingly being deployed in public spaces such as shopping malls, sidewalks, and hospitals, where safe and socially aware navigation depends on anticipating how pedestrians respond to their presence. However, existing datasets rarely capture the full spectrum of robot-induced reactions, e.g., avoidance, neutrality, attraction, which limits progress in modeling these interactions. In this paper, we present the Pedestrian-Robot Interaction~(PeRoI) dataset that captures pedestrian motions categorized into attraction, neutrality, and repulsion across two outdoor sites under three controlled conditions: no robot present, with stationary robot, and with moving robot. This design explicitly reveals how pedestrian behavior varies across robot contexts, and we provide qualitative and quantitative comparisons to established state-of-the-art datasets. Building on these data, we propose the Neural Robot Social Force Model~(NeuRoSFM), an extension of the Social Force Model that integrates neural networks to augment inter-human dynamics with learned components and explicit robot-induced forces to better predict pedestrian motion in vicinity of robots. We evaluate NeuRoSFM by generating trajectories on multiple real-world datasets. The results demonstrate improved modeling of pedestrian-robot interactions, leading to better prediction accuracy, and highlight the value of our dataset and method for advancing socially aware navigation strategies in human-centered environments.

cs.HC↗

arXiv2025

Auditory Localization and Assessment of Consequential Robot Sounds: A Multi-Method Study in Virtual Reality

Mobile robots increasingly operate alongside humans but are often out of sight, so that humans need to rely on the sounds of the robots to recognize their presence. For successful human-robot interaction (HRI), it is therefore crucial to understand how humans perceive robots by their consequential sounds, i.e., operating noise. Prior research suggests that the sound of a quadruped Go1 is more detectable than that of a wheeled Turtlebot. This study builds on this and examines the human ability to localize consequential sounds of three robots (quadruped Go1, wheeled Turtlebot 2i, wheeled HSR) in Virtual Reality. In a within-subjects design, we assessed participants' localization performance for the robots with and without an acoustic vehicle alerting system (AVAS) for two velocities (0.3, 0.8 m/s) and two trajectories (head-on, radial). In each trial, participants were presented with the sound of a moving robot for 3~s and were tasked to point at its final position (localization task). Localization errors were measured as the absolute angular difference between the participants' estimated and the actual robot position. Results showed that the robot type significantly influenced the localization accuracy and precision, with the sound of the wheeled HSR (especially without AVAS) performing worst under all experimental conditions. Surprisingly, participants rated the HSR sound as more positive, less annoying, and more trustworthy than the Turtlebot and Go1 sound. This reveals a tension between subjective evaluation and objective auditory localization performance. Our findings highlight consequential robot sounds as a critical factor for designing intuitive and effective HRI, with implications for human-centered robot design and social navigation.

cs.RO↗

arXiv2025

GRoQ-LoCO: Generalist and Robot-agnostic Quadruped Locomotion Control using Offline Datasets

Recent advancements in large-scale offline training have demonstrated the potential of generalist policy learning for complex robotic tasks. However, applying these principles to legged locomotion remains a challenge due to continuous dynamics and the need for real-time adaptation across diverse terrains and robot morphologies. In this work, we propose GRoQ-LoCO, a scalable, attention-based framework that learns a single generalist locomotion policy across multiple quadruped robots and terrains, relying solely on offline datasets. Our approach leverages expert demonstrations from two distinct locomotion behaviors - stair traversal (non-periodic gaits) and flat terrain traversal (periodic gaits) - collected across multiple quadruped robots, to train a generalist model that enables behavior fusion. Crucially, our framework operates solely on proprioceptive data from all robots without incorporating any robot-specific encodings. The policy is directly deployable on an Intel i7 nuc, producing low-latency control outputs without any test-time optimization. Our extensive experiments demonstrate zero-shot transfer across highly diverse quadruped robots and terrains, including hardware deployment on the Unitree Go1, a commercially available 12kg robot. Notably, we evaluate challenging cross-robot training setups where different locomotion skills are unevenly distributed across robots, yet observe successful transfer of both flat walking and stair traversal behaviors to all robots at test time. We also show preliminary walking on Stoch 5, a 70kg quadruped, on flat and outdoor terrains without requiring any fine tuning. These results demonstrate the potential of offline, data-driven learning to generalize locomotion across diverse quadruped morphologies and behaviors.

cs.RO↗

arXiv2025

Curate, Connect, Inquire: A System for Findable Accessible Interoperable and Reusable (FAIR) Human-Robot Centered Datasets

The rapid growth of AI in robotics has amplified the need for high-quality, reusable datasets, particularly in human-robot interaction (HRI) and AI-embedded robotics. While more robotics datasets are being created, the landscape of open data in the field is uneven. This is due to a lack of curation standards and consistent publication practices, which makes it difficult to discover, access, and reuse robotics data. To address these challenges, this paper presents a curation and access system with two main contributions: (1) a structured methodology to curate, publish, and integrate FAIR (Findable, Accessible, Interoperable, Reusable) human-centered robotics datasets; and (2) a ChatGPT-powered conversational interface trained with the curated datasets metadata and documentation to enable exploration, comparison robotics datasets and data retrieval using natural language. Developed based on practical experience curating datasets from robotics labs within Texas Robotics at the University of Texas at Austin, the system demonstrates the value of standardized curation and persistent publication of robotics data. The system's evaluation suggests that access and understandability of human-robotics data are significantly improved. This work directly aligns with the goals of the HCRL @ ICRA 2025 workshop and represents a step towards more human-centered access to data for embodied AI.

cs.IR↗

arXiv2025

Offensive Robot Cybersecurity

Offensive Robot Cybersecurity introduces a groundbreaking approach by advocating for offensive security methods empowered by means of automation. It emphasizes the necessity of understanding attackers' tactics and identifying vulnerabilities in advance to develop effective defenses, thereby improving robots' security posture. This thesis leverages a decade of robotics experience, employing Machine Learning and Game Theory to streamline the vulnerability identification and exploitation process. Intrinsically, the thesis uncovers a profound connection between robotic architecture and cybersecurity, highlighting that the design and creation aspect of robotics deeply intertwines with its protection against attacks. This duality -- whereby the architecture that shapes robot behavior and capabilities also necessitates a defense mechanism through offensive and defensive cybersecurity strategies -- creates a unique equilibrium. Approaching cybersecurity with a dual perspective of defense and attack, rooted in an understanding of systems architecture, has been pivotal. Through comprehensive analysis, including ethical considerations, the development of security tools, and executing cyber attacks on robot software, hardware, and industry deployments, this thesis proposes a novel architecture for cybersecurity cognitive engines. These engines, powered by advanced game theory and machine learning, pave the way for autonomous offensive cybersecurity strategies for robots, marking a significant shift towards self-defending robotic systems. This research not only underscores the importance of offensive measures in enhancing robot cybersecurity but also sets the stage for future advancements where robots are not just resilient to cyber threats but are equipped to autonomously safeguard themselves.

cs.RO↗