Robot Perception Research Articles

Tunable sensing performance of BiSb monolayer for ethanol induced by strain: A DFT study

Developing tunable gas sensors is pivotal for advancing the embodied perception of robots. In this study, first-principles calculations explore the adjustable sensing capabilities of BiSb monolayers towards ethanol gas under external biaxial and uniaxial strain. The results indicate a reduction in adsorption energy with increasing tensile strain in both biaxial and b-directional uniaxial conditions. Furthermore, strain effectively modulates the electronic structure of the adsorption system. Additionally, computed recovery times suggest that BiSb monolayers face significant challenges in ethanol gas desorption without strain, making them unsuitable for gas sensing under normal conditions. However, under strain, BiSb monolayers demonstrate potential as gas sensors for ethanol detection. These findings introduce novel avenues for enhancing the sensing performance of BiSb monolayers and underscore their prospective application as reversible sensors for ethanol detection.

Motion Trajectory Perception of an Earthworm-Like Robot Using an Inertial Measurement Unit and Autonomous ZUPT

Motion Trajectory Perception of an Earthworm-Like Robot Using an Inertial Measurement Unit and Autonomous ZUPT

Autonomous Robot Navigation Using Adaptive Potential Field

This work presents a novel approach to mobile robot autonomous navigation, utilizing enhanced artificial potential fields in conjunction with path planning,obstacle avoidance and SLAM . In traditional adaptive potential fields, which integrate sensor data to enable robots to navigate safely and efficiently through various environments. The robot's perception of its surroundings, obtained through sensors like cameras, ultrasonic devices, is processed to create a dynamic potential field that reflects the spatial distribution of obstacles, goals, and other relevant features.We propose the design and implementation of an autonomous robot-based real-time vision-based system for detecting and tracking features in a structured environment ensuring robust obstacle avoidance and path planning. Keywords- Adaptive Potential Fields, Autonomous Navigation, Autonomous Systems, Machine Learning in Robotics, Motor driver, Obstacle Avoidance, Path Planning, Raspberry Pi, Real-time Adaptation, ROS (Robot Operating System), Sensor-based Navigation, Servo Motor, SLAM (Simultaneous Localization and Mapping),Ultrasonic Sensors

Robotic Arm Perception: An Eyetracking Study Exploring Causal Relations and Perceived Trust

With the increased integration of robots into industrial, service, and educational spaces, it is imperative to understand factors that influence human-robot interactions. While the existing literature has explored human perception of robot-produced verbal communication, there is a notable gap in understanding how adults process robot-produced nonverbal communication. Current research focuses on subjective measures of the individual’s perception of robots during interactions, but often fails to have objective measures or control groups in their designs which limits the conclusions that can be made. In this study, we aim to implement a design that allows for the comparison of robot and human nonverbal communication understanding and perceiving through both objective eye-tracking measures and subjective survey measures. This design should prove useful to gain insights into how people understand information provided by robots and humans when watching their nonverbal gestures, and their perceptions of the agents after their interactions.

Improving the Social Well-Being of Single Older Adults Using the LOVOT Social Robot: Qualitative Phenomenological Study.

This study examined the social well-being of single older adults through the companionship of a social robot, LOVOT (Love+Robot; Groove X). It is designed as a companion for older adults, providing love and affection through verbal and physical interaction. We investigated older adults' perceptions of the technology and how they benefitted from interacting with LOVOT, to guide the future development of social robots. This study aimed to use a phenomenological research design to understand the participants' experiences of companionship provided by the social robot. Our research focused on (1) examining the social well-being of single older adults through the companionship of social robots and (2) understanding the perceptions of single older adults when interacting with social robots. Given the prevalence of technology use to support aging, understanding single older adults' social well-being and their perceptions of social robots is essential to guide future research on and design of social robots. A total of 5 single women, aged 60 to 75 years, participated in the study. The participants interacted independently with the robot for a week in their own homes and then participated in a poststudy interview to share their experiences. In total, 4 main themes emerged from the participants' interactions with LOVOT, such as caring for a social robot, comforting presence of the social robot, meaningful connections with the social robot, and preference for LOVOT over pets. The results indicate that single older adults can obtain psychosocial support by interacting with LOVOT. LOVOT is easily accepted as a companion and makes single older adults feel like they have a greater sense of purpose and someone to connect with. This study suggests that social robots can provide companionship to older adults who live alone. Social robots can help alleviate loneliness by allowing single older adults to form social connections with robots as companions. These findings are particularly important given the rapid aging of the population and the increasing number of single-person households in Singapore.

Is human perception of AI robots introducing a new type of bias?

Is human perception of AI robots introducing a new type of bias?

The Multi-Modal Robot Perception, Language Information, and Environment Prediction Model Based on Deep Learning

This paper presents a deep learning-based model to enhance robot perception and prediction in complex environments. The model utilizes CNN, LSTM, and attention mechanisms to address multimodal perception and environment prediction tasks. The introduction provides background on the importance of robots in various fields and emphasizes the need for multimodal perception. The methodology section explains the working principles of CNN, LSTM, and attention mechanisms. The experimental results demonstrate significant improvements in robot perception and prediction, with low errors and high accuracy. The proposed model supports robot applications in complex environments such as autonomous navigation and human-robot interaction. It effectively handles multimodal data and enhances robot perception and prediction capabilities. The experimental results validate the model's effectiveness, providing important support for robot decision-making in complex environments.

Flexible Arc-Shaped Micro-Fiber Bragg Grating Array Three-Dimensional Tactile Sensor for Fingertip Signals Detection and Human Pulse Monitoring.

A flexible arc-shaped micro-Fiber Bragg Grating (mFBG) array three-dimensional tactile sensor for fingertip signal detection and human pulse monitoring is presented. It is based on a three mFBGs array which is embedded in an arc-shaped poly (dimethylsiloxane) (PDMS) elastomer, which can effectively discriminate the normal force, left force, and right force by monitoring the reflected intensity variation of the three mFBGs. Different from the traditional FBG sensors, this sensor measures force by detecting changes in light intensity, effectively avoiding the wavelength cross-sensitivity impact of temperature variations on the sensor performance. This design strategy simplifies the sensor structure, reduces the system complexity and signal interrogation cost, and enhances reliability and practicality. Through systematic experiments, we successfully validated the sensor's superior performance, achieving a minimum detection force of 0.01 N and providing robust data support for practical applications. In addition, the sensor has been used to monitor human pulse accurately. The successful fabrication and experimental validation of this sensor lay a foundation for its widespread application in fields such as robot perception and human vital signal detection.

Public perceptions of care robots

Public perceptions of care robots

A stretchable and self-powered strain sensor with elastomeric electret

Stretchable deformation sensors play an important role in the perception and autonomy of soft robots. While various sensors have been reported, the mechanical sensor with self-powered capability and stretchability to suit extreme environment is urgently required. In this study, a stretchable, self-powered, sensitivity adjustable, and long-term stability strain sensor based on the stretchable electret is researched for deformation monitoring. This stretchable and self-powered sensor is composed of the dielectric elastomer with designed elastic modulus gradient and the stretchable electret with elastomeric and Nano-particles. The electro-mechanical capability of this designed sensor is researched and optimized by multi-objective optimization approach. The sensor exhibits self-powered capability, stretchability (tensile strain from 0% to 20%), tunable sensitivity (optimized from 5.7 to 5.9 pC mm−1), and stability, where electromechanical performance remains stable after 8000 stretching cycles. The feasibility of its autonomous sensing which promotes a wide range of potential application in soft robotics is demonstrated. This elastic modulus gradient-induced and net charge design significantly broadened the potential applications with large deformation and low-attached stiffness.

A vision transformer‐based robotic perception for early tea chrysanthemum flower counting in field environments

A vision transformer‐based robotic perception for early tea chrysanthemum flower counting in field environments

Bionic e-skin with precise multi-directional droplet sliding sensing for enhanced robotic perception

Electronic skins with deep and comprehensive liquid information detection are desired to endow intelligent robotic devices with augmented perception and autonomous regulation in common droplet environments. At present, one technical limitation of electronic skins is the inability to perceive the liquid sliding information as realistically as humans and give feedback in time. To this critical challenge, in this work, a self-powered bionic droplet electronic skin is proposed by constructing an ingenious co-layer interlaced electrode network and using an overpass connection method. The bionic skin is used for droplet environment reconnaissance and converts various dynamic droplet sliding behaviors into electrical signals based on triboelectricity. More importantly, the two-dimensional sliding behavior of liquid droplets is comprehensively perceived by the e-skin and visually fed back in real-time on an indicator. Furthermore, the flow direction warning and intelligent closed-loop control of water leakage are also achieved by this e-skin, achieving the effect of human neuromodulation. This strategy compensates for the limitations of e-skin sensing droplets and greatly narrows the gap between artificial e-skins and human skins in perceiving functions.

Influence of Simulation and Interactivity on Human Perceptions of a Robot During Navigation Tasks

In Human-Robot Interaction, researchers typically utilize in-person studies to collect subjective perceptions of a robot. In addition, videos of interactions and interactive simulations (where participants control an avatar that interacts with a robot in a virtual world) have been used to quickly collect human feedback at scale. How would human perceptions of robots compare between these methodologies? To investigate this question, we conducted a 2x2 between-subjects study (N=160), which evaluated the effect of the interaction environment (Real vs. Simulated environment) and participants’ interactivity during human-robot encounters (Interactive participation vs. Video observations) on perceptions about a robot (competence, discomfort, social presentation, and social information processing) for the task of navigating in concert with people. We also studied participants’ workload across the experimental conditions. Our results revealed a significant difference in the perceptions of the robot between the real environment and the simulated environment. Furthermore, our results showed differences in human perceptions when people watched a video of an encounter versus taking part in the encounter. Finally, we found that simulated interactions and videos of the simulated encounter resulted in a higher workload than real-world encounters and videos thereof. Our results suggest that findings from video and simulation methodologies may not always translate to real-world human-robot interactions. In order to allow practitioners to leverage learnings from this study and future researchers to expand our knowledge in this area, we provide guidelines for weighing the tradeoffs between different methodologies.

Therapists’ perspective on acceptance of robot-assisted physical rehabilitation in a middle-income country: a study from Vietnam

Robot-assisted physical rehabilitation offers promising benefits for patients, yet its adoption among therapists remains a complex challenge. This study investigates the acceptance of robot-assisted physical rehabilitation technology among therapists in Vietnam, a middle-income country with a growing demand for rehabilitation services. Drawing on the Technology Acceptance Model 2 (TAM2) and the Theory of Planned Behaviour (TPB), an online survey and semi-structured interviews were conducted to explore therapists’ attitudes and intentions towards using this technology. The results show that Vietnamese therapists recognised its potential benefits and expressed a willingness to use it. Although having similar acceptance patterns compared to developed regions, they demonstrated significantly higher levels of agreement across acceptance constructs. This may be attributed to factors such as the novelty effect, cultural perceptions of robots, and the high workload of therapists in Vietnam. Gender and location were found to influence two acceptance constructs–subjective norms and image, respectively–highlighting the need for tailored strategies in technology implementation. The study underscores the importance of considering socio-cultural factors in the adoption of technology and provides insights for enhancing the acceptance and effectiveness of robot-assisted physical rehabilitation in Vietnam. This contributes to the global understanding of therapist acceptance of technology in this field.

Polarimetric Imaging for Robot Perception: A Review.

In recent years, the integration of polarimetric imaging into robotic perception systems has increased significantly, driven by the accessibility of affordable polarimetric sensors. This technology complements traditional color imaging by capturing and analyzing the polarization characteristics of light. This additional information provides robots with valuable insights into object shape, material composition, and other properties, ultimately enabling more robust manipulation tasks. This review aims to provide a comprehensive analysis of the principles behind polarimetric imaging and its diverse applications within the field of robotic perception. By exploiting the polarization state of light, polarimetric imaging offers promising solutions to three key challenges in robot vision: Surface segmentation; depth estimation through polarization patterns; and 3D reconstruction using polarimetric data. This review emphasizes the practical value of polarimetric imaging in robotics by demonstrating its effectiveness in addressing real-world challenges. We then explore potential applications of this technology not only within the core robotics field but also in related areas. Through a comparative analysis, our goal is to elucidate the strengths and limitations of polarimetric imaging techniques. This analysis will contribute to a deeper understanding of its broad applicability across various domains within and beyond robotics.

Meeting the needs of physically disabled tourists: use of service robots toward the hotel attachment

满足身体残障游客的需求：酒店服务机器人在促进酒店情感联系中的应用研究目的本研究关注身体残障游客, 并检查他们对酒店服务机器人的感知, 以及对整体体验、行为意图和酒店情感联系的影响。研究方法通过Prolific平台进行调查, 收集了409名受访者的数据。数据使用PLS-SEM进行分析。研究发现显著的PLS-SEM结果表明, 感知安全性和功能性收益显著影响酒店的整体体验和再次入住意愿。感知隐私与酒店的整体体验和再次入住意愿无关。此外, 酒店的整体体验显著影响再次入住意愿, 进而影响酒店情感联系。研究局限性本研究仅考察了身体残障游客对酒店服务机器人的感知。研究结果基于调查, 因此无法推广到整体。研究创新当前研究表明再次入住意愿和酒店情感联系的影响。此外, 本研究探讨了整体酒店体验在感知隐私、感知安全和功能性收益与再次入住意愿之间的关联中的中介作用。

Dual-mode temperature sensor based on ferroelectric Bi0.5Na0.5TiO3 materials for robotic tactile perception

Temperature perception can allow robots to acquire rich information regarding their surroundings to improve the accuracy of both object manipulation and object recognition. However, existing robotic temperature perception systems mainly depend on thermoresistive effect, suffering from singular functionality and high energy consumption. Here, we demonstrate a ferroelectric Bi0.5Na0.5TiO3-based dual-mode temperature sensor for robotic temperature perception. The dual-mode temperature sensor can operate in a passive mode to perceive temperature variations by generating pyroelectric signals, and work in an active mode to detect static temperatures by producing pyro-photoelectric signals. Equipped with the dual-mode temperature sensor, a robot hand can perceive multi-dimensional temperature information and assess the thermal conductivity of materials, allowing identification of different materials in real-time with a high accuracy of 97.2 %. By combining passive and active temperature perception, the proposed dual-mode temperature sensor therefore holds significant potential for future robotic perception and manipulation.

Integrating sound effects and background music in Robotic storytelling – A series of online studies across different story genres

Social robots as storytellers combine advantages of human storytellers – such as embodiment, gestures, and gaze – and audio books – large repertoire of voices, sound effects, and background music. However, research on adding non-speech sounds to robotic storytelling is yet in its infancy. The current series of four online studies investigates the influence of sound effects and background music in robotic storytelling on recipients’ storytelling experience and enjoyment, robot perception, and emotion induction across different story genres, i.e. horror, detective, romantic and humorous stories. Results indicate increased enjoyment for romantic stories and a trend for decreased fatigue for all genres when adding sound effects and background music to the robotic storytelling. Of the four genres examined, horror stories seem to benefit the most from the addition of non-speech sounds. Future research should provide guidelines for the selection of music and sound effects to improve the realization of non-speech sound-accompanied robotic storytelling. In conclusion, our ongoing research suggests that the integration of sound effects and background music holds promise for enhancing robotic storytelling, and our genre comparison provides first guidance of when to use them.

Social robots are good for me, but better for other people：The presumed allo-enhancement effect of social robot perceptions

This research proposes and investigates the presumed allo-enhancement effect of social robot perceptions, a tendency for individuals to view social robots as more beneficial for others than for themselves. We discuss this as a systematic bias in the perception of the utility of social robots. Through two survey studies, we test and replicate self-other perceptual differences, obtain effect sizes of these perceptual differences, and trace the impact of this presumed allo-enhancement effect on individuals' attitudes and behaviors. Analyses revealed strong perceptual differences, where individuals consistently believed social robots to be more enhancing for others than for themselves (d = −0.69, d = −0.62). These perceptual differences predicted individuals’ attitudes and endorsed behaviors towards social robots. By identifying this bias, we offer a new theoretical lens for understanding how people perceive and respond to emergent technologies.

Designing Socially Assistive Robots Exploring Israeli and German Designers’ Perceptions

Socially assistive robots (SARs) are becoming more prevalent in everyday life, emphasizing the need to make them socially acceptable and aligned with users’ expectations. Robots’ appearance impacts users’ behaviors and attitudes toward them. Therefore, product designers choose visual qualities to give the robot a character and to imply its functionality and personality. In this work, we sought to investigate the effect of cultural differences on Israeli and German designers’ perceptions of SARs’ roles and appearance in four different contexts: a service robot for an assisted living/retirement residence facility, a medical assistant robot for a hospital environment, a COVID-19 officer robot, and a personal assistant robot for domestic use. The key insight is that although Israeli and German designers share similar perceptions of visual qualities for most of the robotics roles, we found differences in the perception of the COVID-19 officer robot’s role and, by that, its most suitable visual design. This work indicates that context and culture play a role in users’ perceptions and expectations; therefore, they should be taken into account when designing new SARs for diverse contexts.