arXiv subjects

Robotics

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

Follow this topic with RSS

At least 919 records · Page 51Linked to original sources

arXiv2023

Maximising Coefficiency of Human-Robot Handovers through Reinforcement Learning

Handing objects to humans is an essential capability for collaborative robots. Previous research works on human-robot handovers focus on facilitating the performance of the human partner and possibly minimising the physical effort needed to grasp the object. However, altruistic robot behaviours may result in protracted and awkward robot motions, contributing to unpleasant sensations by the human partner and affecting perceived safety and social acceptance. This paper investigates whether transferring the cognitive science principle that "humans act coefficiently as a group" (i.e. simultaneously maximising the benefits of all agents involved) to human-robot cooperative tasks promotes a more seamless and natural interaction. Human-robot coefficiency is first modelled by identifying implicit indicators of human comfort and discomfort as well as calculating the robot energy consumption in performing the desired trajectory. We then present a reinforcement learning approach that uses the human-robot coefficiency score as reward to adapt and learn online the combination of robot interaction parameters that maximises such coefficiency. Results proved that by acting coefficiently the robot could meet the individual preferences of most subjects involved in the experiments, improve the human perceived comfort, and foster trust in the robotic partner.

cs.RO↗

arXiv2025

On the causality between affective impact and coordinated human-robot reactions

In an effort to improve how robots function in social contexts, this paper investigates if a robot that actively shares a reaction to an event with a human alters how the human perceives the robot's affective impact. To verify this, we created two different test setups. One to highlight and isolate the reaction element of affective robot expressions, and one to investigate the effects of applying specific timing delays to a robot reacting to a physical encounter with a human. The first test was conducted with two different groups (n=84) of human observers, a test group and a control group both interacting with the robot. The second test was performed with 110 participants using increasingly longer reaction delays for the robot with every ten participants. The results show a statistically significant change (p$<$.05) in perceived affective impact for the robots when they react to an event shared with a human observer rather than reacting at random. The result also shows for shared physical interaction, the near-human reaction times from the robot are most appropriate for the scenario. The paper concludes that a delay time around 200ms may render the biggest impact on human observers for small-sized non-humanoid robots. It further concludes that a slightly shorter reaction time around 100ms is most effective when the goal is to make the human observers feel they made the biggest impact on the robot.

cs.RO↗

arXiv2025

Ice-Breakers, Turn-Takers and Fun-Makers: Exploring Robots for Groups with Teenagers

Successful, enjoyable group interactions are important in public and personal contexts, especially for teenagers whose peer groups are important for self-identity and self-esteem. Social robots seemingly have the potential to positively shape group interactions, but it seems difficult to effect such impact by designing robot behaviors solely based on related (human interaction) literature. In this article, we take a user-centered approach to explore how teenagers envisage a social robot "group assistant". We engaged 16 teenagers in focus groups, interviews, and robot testing to capture their views and reflections about robots for groups. Over the course of a two-week summer school, participants co-designed the action space for such a robot and experienced working with/wizarding it for 10+ hours. This experience further altered and deepened their insights into using robots as group assistants. We report results regarding teenagers' views on the applicability and use of a robot group assistant, how these expectations evolved throughout the study, and their repeat interactions with the robot. Our results indicate that each group moves on a spectrum of need for the robot, reflected in use of the robot more (or less) for ice-breaking, turn-taking, and fun-making as the situation demanded.

cs.RO↗

arXiv2024

From One to Many: How Active Robot Swarm Sizes Influence Human Cognitive Processes

In robotics, understanding human interaction with autonomous systems is crucial for enhancing collaborative technologies. We focus on human-swarm interaction (HSI), exploring how differently sized groups of active robots affect operators' cognitive and perceptual reactions over different durations. We analyze the impact of different numbers of active robots within a 15-robot swarm on operators' time perception, emotional state, flow experience, and task difficulty perception. Our findings indicate that managing multiple active robots when compared to one active robot significantly alters time perception and flow experience, leading to a faster passage of time and increased flow. More active robots and extended durations cause increased emotional arousal and perceived task difficulty, highlighting the interaction between robot the number of active robots and human cognitive processes. These insights inform the creation of intuitive human-swarm interfaces and aid in developing swarm robotic systems aligned with human cognitive structures, enhancing human-robot collaboration.

cs.RO↗

arXiv2025

Help or Hindrance: Understanding the Impact of Robot Communication in Action Teams

The human-robot interaction (HRI) field has recognized the importance of enabling robots to interact with teams. Human teams rely on effective communication for successful collaboration in time-sensitive environments. Robots can play a role in enhancing team coordination through real-time assistance. Despite significant progress in human-robot teaming research, there remains an essential gap in how robots can effectively communicate with action teams using multimodal interaction cues in time-sensitive environments. This study addresses this knowledge gap in an experimental in-lab study to investigate how multimodal robot communication in action teams affects workload and human perception of robots. We explore team collaboration in a medical training scenario where a robotic crash cart (RCC) provides verbal and non-verbal cues to help users remember to perform iterative tasks and search for supplies. Our findings show that verbal cues for object search tasks and visual cues for task reminders reduce team workload and increase perceived ease of use and perceived usefulness more effectively than a robot with no feedback. Our work contributes to multimodal interaction research in the HRI field, highlighting the need for more human-robot teaming research to understand best practices for integrating collaborative robots in time-sensitive environments such as in hospitals, search and rescue, and manufacturing applications.

cs.HC↗

arXiv2017

A Framework for Interactive Teaching of Virtual Borders to Mobile Robots

The increasing number of robots in home environments leads to an emerging coexistence between humans and robots. Robots undertake common tasks and support the residents in their everyday life. People appreciate the presence of robots in their environment as long as they keep the control over them. One important aspect is the control of a robot's workspace. Therefore, we introduce virtual borders to precisely and flexibly define the workspace of mobile robots. First, we propose a novel framework that allows a person to interactively restrict a mobile robot's workspace. To show the validity of this framework, a concrete implementation based on visual markers is implemented. Afterwards, the mobile robot is capable of performing its tasks while respecting the new virtual borders. The approach is accurate, flexible and less time consuming than explicit robot programming. Hence, even non-experts are able to teach virtual borders to their robots which is especially interesting in domains like vacuuming or service robots in home environments.

cs.RO↗

arXiv2020

Autonomous Intruder Detection Using a ROS-Based Multi-Robot System Equipped with 2D-LiDAR Sensors

The application of autonomous mobile robots in robotic security platforms is becoming a promising field of innovation due to their adaptive capability of responding to potential disturbances perceived through a wide range of sensors. Researchers have proposed systems that either focus on utilizing a single mobile robot or a system of cooperative multiple robots. However, very few of the proposed works, particularly in the field of multi-robot systems, are completely dependent on LiDAR sensors for achieving various tasks. This is essential when other sensors on a robot fail to provide peak performance in particular conditions, such as a camera operating in the absence of light. This paper proposes a multi-robot system that is developed using ROS (Robot Operating System) for intruder detection in a single-range-sensor-per-robot scenario with centralized processing of detections from all robots by our central bot MIDNet (Multiple Intruder Detection Network). This work is aimed at providing an autonomous multi-robot security solution for a warehouse in the absence of human personnel.

cs.RO↗

arXiv2025

Environment-Centric Learning Approach for Gait Synthesis in Terrestrial Soft Robots

Locomotion gaits are fundamental for control of soft terrestrial robots. However, synthesis of these gaits is challenging due to modeling of robot-environment interaction and lack of a mathematical framework. This work presents an environment-centric, data-driven and fault-tolerant probabilistic Model-Free Control (pMFC) framework that allows for soft multi-limb robots to learn from their environment and synthesize diverse sets of locomotion gaits for realizing open-loop control. Here, discretization of factors dominating robot-environment interactions enables an environment-specific graphical representation where the edges encode experimental locomotion data corresponding to the robot motion primitives. In this graph, locomotion gaits are defined as simple cycles that are transformation invariant, i.e., the locomotion is independent of the starting vertex of these periodic cycles. Gait synthesis, the problem of finding optimal locomotion gaits for a given substrate, is formulated as Binary Integer Linear Programming (BILP) problems with a linearized cost function, linear constraints, and iterative simple cycle detection. Experimentally, gaits are synthesized for varying robot-environment interactions. Variables include robot morphology - three-limb and four-limb robots, TerreSoRo-III and TerreSoRo-IV; substrate - rubber mat, whiteboard and carpet; and actuator functionality - simulated loss of robot limb actuation. On an average, gait synthesis improves the translation and rotation speeds by 82% and 97% respectively. The results highlight that data-driven methods are vital to soft robot locomotion control due to the significant influence of unexpected asymmetries in the system and the dependence of optimal gait sequences on the experimental robot-environment interaction.

cs.RO↗

arXiv2022

Using Design Metaphors to Understand User Expectations of Socially Interactive Robot Embodiments

The physical design of a robot suggests expectations of that robot's functionality for human users and collaborators. When those expectations align with the true capabilities of the robot, interaction with the robot is enhanced. However, misalignment of those expectations can result in an unsatisfying interaction. This paper uses Mechanical Turk to evaluate user expectation through the use of design metaphors as applied to a wide range of robot embodiments. The first study (N=382) associates crowd-sourced design metaphors to different robot embodiments. The second study (N=803) assesses initial social expectations of robot embodiments. The final study (N=805) addresses the degree of abstraction of the design metaphors and the functional expectations projected on robot embodiments. Together, these results can guide robot designers toward aligning user expectations with true robot capabilities, facilitating positive human-robot interaction.

cs.RO↗

arXiv2024

Toward Family-Robot Interactions: A Family-Centered Framework in HRI

As robotic products become more integrated into daily life, there is a greater need to understand authentic and real-world human-robot interactions to inform product design. Across many domestic, educational, and public settings, robots interact with not only individuals and groups of users, but also families, including children, parents, relatives, and even pets. However, products developed to date and research in human-robot and child-robot interactions have focused on the interaction with their primary users, neglecting the complex and multifaceted interactions between family members and with the robot. There is a significant gap in knowledge, methods, and theories for how to design robots to support these interactions. To inform the design of robots that can support and enhance family life, this paper provides (1) a narrative review exemplifying the research gap and opportunities for family-robot interactions and (2) an actionable family-centered framework for research and practices in human-robot and child-robot interaction.

cs.RO↗

arXiv2024

Human Impression of Humanoid Robots Mirroring Social Cues

Mirroring non-verbal social cues such as affect or movement can enhance human-human and human-robot interactions in the real world. The robotic platforms and control methods also impact people's perception of human-robot interaction. However, limited studies have compared robot imitation across different platforms and control methods. Our research addresses this gap by conducting two experiments comparing people's perception of affective mirroring between the iCub and Pepper robots and movement mirroring between vision-based iCub control and Inertial Measurement Unit (IMU)-based iCub control. We discovered that the iCub robot was perceived as more humanlike than the Pepper robot when mirroring affect. A vision-based controlled iCub outperformed the IMU-based controlled one in the movement mirroring task. Our findings suggest that different robotic platforms impact people's perception of robots' mirroring during HRI. The control method also contributes to the robot's mirroring performance. Our work sheds light on the design and application of different humanoid robots in the real world.

cs.RO↗

arXiv2024

Giving Robots a Voice: Human-in-the-Loop Voice Creation and open-ended Labeling

Speech is a natural interface for humans to interact with robots. Yet, aligning a robot's voice to its appearance is challenging due to the rich vocabulary of both modalities. Previous research has explored a few labels to describe robots and tested them on a limited number of robots and existing voices. Here, we develop a robot-voice creation tool followed by large-scale behavioral human experiments (N=2,505). First, participants collectively tune robotic voices to match 175 robot images using an adaptive human-in-the-loop pipeline. Then, participants describe their impression of the robot or their matched voice using another human-in-the-loop paradigm for open-ended labeling. The elicited taxonomy is then used to rate robot attributes and to predict the best voice for an unseen robot. We offer a web interface to aid engineers in customizing robot voices, demonstrating the synergy between cognitive science and machine learning for engineering tools.

cs.HC↗

arXiv2024

Should I Help a Delivery Robot? Cultivating Prosocial Norms through Observations

We propose leveraging prosocial observations to cultivate new social norms to encourage prosocial behaviors toward delivery robots. With an online experiment, we quantitatively assess updates in norm beliefs regarding human-robot prosocial behaviors through observational learning. Results demonstrate the initially perceived normativity of helping robots is influenced by familiarity with delivery robots and perceptions of robots' social intelligence. Observing human-robot prosocial interactions notably shifts peoples' normative beliefs about prosocial actions; thereby changing their perceived obligations to offer help to delivery robots. Additionally, we found that observing robots offering help to humans, rather than receiving help, more significantly increased participants' feelings of obligation to help robots. Our findings provide insights into prosocial design for future mobility systems. Improved familiarity with robot capabilities and portraying them as desirable social partners can help foster wider acceptance. Furthermore, robots need to be designed to exhibit higher levels of interactivity and reciprocal capabilities for prosocial behavior.

cs.RO↗

arXiv2025

Predicting Human Perceptions of Robot Performance During Navigation Tasks

Understanding human perceptions of robot performance is crucial for designing socially intelligent robots that can adapt to human expectations. Current approaches often rely on surveys, which can disrupt ongoing human-robot interactions. As an alternative, we explore predicting people's perceptions of robot performance using non-verbal behavioral cues and machine learning techniques. We contribute the SEAN TOGETHER Dataset consisting of observations of an interaction between a person and a mobile robot in Virtual Reality, together with perceptions of robot performance provided by users on a 5-point scale. We then analyze how well humans and supervised learning techniques can predict perceived robot performance based on different observation types (like facial expression and spatial behavior features). Our results suggest that facial expressions alone provide useful information, but in the navigation scenarios that we considered, reasoning about spatial features in context is critical for the prediction task. Also, supervised learning techniques outperformed humans' predictions in most cases. Further, when predicting robot performance as a binary classification task on unseen users' data, the F1-Score of machine learning models more than doubled that of predictions on a 5-point scale. This suggested good generalization capabilities, particularly in identifying performance directionality over exact ratings. Based on these findings, we conducted a real-world demonstration where a mobile robot uses a machine learning model to predict how a human who follows it perceives it. Finally, we discuss the implications of our results for implementing these supervised learning models in real-world navigation. Our work paves the path to automatically enhancing robot behavior based on observations of users and inferences about their perceptions of a robot.

cs.RO↗

arXiv2023

A wireless signal-based sensing framework for robotics

In this paper we develop the analytical framework for a novel Wireless signal-based Sensing capability for Robotics (WSR) by leveraging robots' mobility. It allows robots to primarily measure relative direction, or Angle-of-Arrival (AOA), to other robots, while operating in non-line-of-sight unmapped environments and without requiring external infrastructure. We do so by capturing all of the paths that a wireless signal traverses as it travels from a transmitting to a receiving robot in the team, which we term as an AOA profile. The key intuition behind our approach is to enable a robot to emulate antenna arrays as it moves freely in 2D and 3D space. The small differences in the phase of the wireless signals are thus processed with knowledge of robots' local displacement to obtain the profile, via a method akin to Synthetic Aperture Radar (SAR). The main contribution of this work is the development of i) a framework to accommodate arbitrary 2D and 3D motion, as well as continuous mobility of both signal transmitting and receiving robots, while computing AOA profiles between them and ii) a Cramer-Rao Bound analysis, based on antenna array theory, that provides a lower bound on the variance in AOA estimation as a function of the geometry of robot motion. We show that allowing robots to use their full mobility in 3D space while performing SAR, results in more accurate AOA profiles and thus better AOA estimation. All analytical developments are substantiated by extensive simulation and hardware experiments on air/ground robot platforms using 5 GHz WiFi. Our experimental results bolster our analytical findings, demonstrating that 3D motion provides enhanced and consistent accuracy, with total AOA error of less than 10 degree for 95% of trials. We also analytically characterize the impact of displacement estimation errors on the measured AOA.

cs.RO↗

arXiv2025

A Modal-Space Formulation for Momentum Observer Contact Estimation and Effects of Uncertainty for Continuum Robots

Contact detection for continuum and soft robots has been limited in past works to statics or kinematics-based methods with assumed circular bending curvature or known bending profiles. In this paper, we adapt the generalized momentum observer contact estimation method to continuum robots. This is made possible by leveraging recent results for real-time shape sensing of continuum robots along with a modal-space representation of the robot dynamics. In addition to presenting an approach for estimating the generalized forces due to contact via a momentum observer, we present a constrained optimization method to identify the wrench imparted on the robot during contact. We also present an approach for investigating the effects of unmodeled deviations in the robot's dynamic state on the contact detection method and we validate our algorithm by simulations and experiments. We also compare the performance of the momentum observer to the joint force deviation method, a direct estimation approach using the robot's full dynamic model. We also demonstrate a basic extension of the method to multisegment continuum robots. Results presented in this work extend dynamic contact detection to the domain of continuum and soft robots and can be used to improve the safety of large-scale continuum robots for human-robot collaboration.

cs.RO↗

arXiv2022

Designs, Motion Mechanism, Motion Coordination, and Communication of Bionic Robot Fishes: A Survey

In the last few years, there have been many new developments and significant accomplishments in the research of bionic robot fishes. However, in terms of swimming performance, existing bionic robot fishes lag far behind fish, prompting researchers to constantly develop innovative designs of various bionic robot fishes. In this paper, the latest designs of robot fishes are presented in detail, distinguished by the propulsion mode. New robot fishes mainly include soft robot fishes and rigid-soft coupled robot fishes. The latest progress in the study of the swimming mechanism is analyzed on the basis of summarizing the main swimming theories of fish. The current state-of-the-art research in the new field of motion coordination and communication of multiple robot fishes is summarized. The general research trend in robot fishes is to utilize more efficient and robust methods to best mimic real fish while exhibiting superior swimming performance. The current challenges and potential future research directions are discussed. Various methods are needed to narrow the gap in swimming performance between robot fishes and fish. This paper is a first step to bring together roboticists and marine biologists interested in learning state-of-the-art research on bionic robot fishes.

cs.RO↗

arXiv2023

Robotics Applications in Neurology: A Review of Recent Advancements and Future Directions

Robotic technology has the potential to revolutionize the field of neurology by providing new methods for diagnosis, treatment, and rehabilitation of neurological disorders. In recent years, there has been an increasing interest in the development of robotics applications for neurology, driven by advances in sensing, actuation, and control systems. This review paper provides a comprehensive overview of the recent advancements in robotics technology for neurology, with a focus on three main areas: diagnosis, treatment, and rehabilitation. In the area of diagnosis, robotics has been used for developing new imaging techniques and tools for more accurate and non-invasive mapping of brain structures and functions. For treatment, robotics has been used for developing minimally invasive surgical procedures, including stereotactic and endoscopic approaches, as well as for the delivery of therapeutic agents to specific targets in the brain. In rehabilitation, robotics has been used for developing assistive devices and platforms for motor and cognitive training of patients with neurological disorders. The paper also discusses the challenges and limitations of current robotics technology for neurology, including the need for more reliable and precise sensing and actuation systems, the development of better control algorithms, and the ethical implications of robotic interventions in the human brain. Finally, the paper outlines future directions and opportunities for robotics applications in neurology, including the integration of robotics with other emerging technologies, such as neuroprosthetics, artificial intelligence, and virtual reality. Overall, this review highlights the potential of robotics technology to transform the field of neurology and improve the lives of patients with neurological disorders.

cs.RO↗