Robot Experiments Research Articles

Pseudo-eating behavior of service robot to improve the trustworthiness of product recommendations

Service robots are used in a range of service situations, including those where they are required to recommend products. However, when robots use subjective statements for product recommendations, users may not place sufficient trust in the robot's recommendations which may degrade its recommendation capability. This study proposes a method to improve the perceived trustworthiness of the robot's subjective statements and thus promote the user's food purchase behavior. The proposed recommendation method involves explicitly sharing the robot's experience with the user and improving the perceived trustworthiness of the robot's subjective statements by performing a pseudo-eating behavior. The results of two online surveys show that the robot's statements and recommendations accompanied by the pseudo-eating behavior improved the trust in the robot's subjective statement and consequently, the user's willingness to purchase products. Further, the results of a field experiment in a real bakery store showed that the proposed method is comparable to recommendation methods proposed in previous works. The results obtained through the online surveys and the field experiment indicate that robots are trusted even when they express subjective statements for recommendations by explicitly sharing the experience with the user.

Information-Theoretic Exploration for Adaptive Robotic Grasping in Clutter Based on Real-Time Pixel-Level Grasp Detection

Robust robotic grasping in clutter still remains a challenging problem despite its great practical value. This paper presents an information-theoretic exploration approach that aims to improve the grasp estimation quality in complex environment. First, a light-weight grasp detector that composed by inverted residual blocks and a pyramid pooling module is proposed to make more accurate pixel-wise predictions in real-time, which are then projected to the workspace. To measure the uncertainty of estimations in the workspace, a two-dimensional grasp entropy (2D-GE) is defined and Gaussian process is applied to regress the variation of the information gain that approximated by 2D-GEs. Finally, guided by the information gain, depth cost, and distance cost, our approach is able to actively collect and fuse estimations from multiple informative viewpoints through the exploration that finally converges to a refined best grasp, resulting an improved grasping performance with a higher success rate and environmental adaptability in clutter. Simulation and robotic experiment are both performed to demonstrate the effectiveness of our method and compare it to baselines.

Consumers’ Brand Experiences With Robotic Service Failures: Modeling Appraisal, Attribution, and Psychological Reactance

Due to the stretched capacities of human staff, consumers are increasingly placed in situations where they are “required” to use technology amidst their travel experiences, despite potential service failures in robotic technologies. Yet, research into how robotic service failures could potentially spill over to consumers’ brand experiences, robot experiences, and adoption intention remains unexplored. Drawing on appraisal, attribution, and psychological reactance theories, an interconnected research model of service failure, attribution, and (in)voluntary robot adoption was tested via two experiments. A mixed-design quasi-experiment (Study 1) found a significant interaction effect of service failure and attribution on brand experience in the hotel concierge context. Study 2 provided further evidence for the causal effects in Study 1 by employing a between-subject quasi-experiment in the hotel front desk context with additional measures. Collectively, this research contributes to the literature by highlighting how temporal, situational, and contextual factors in HRI may impact evaluations of brand and robot experiences.

Spatial navigation method based on the entorhinal-hippocampal-prefrontal information transmission circuit of rat's brain

Physiological studies have revealed that rats perform spatial localization relying on grid cells and place cells in the entorhinal-hippocampal CA3 structure. The dynamic connection between the entorhinal-hippocampal structure and the prefrontal cortex is crucial for navigation. Based on these findings, this paper proposes a spatial navigation method based on the entorhinal-hippocampal-prefrontal information transmission circuit of the rat's brain, with the aim of endowing the mobile robot with strong spatial navigation capability. Using the hippocampal CA3-prefrontal spatial navigation model as a foundation, this paper constructed a dynamic self-organizing model with the hippocampal CA1 place cells as the basic unit to optimize the navigation path. The path information was then fed back to the impulse neural network via hippocampal CA3 place cells and prefrontal cortex action neurons, improving the convergence speed of the model and helping to establish long-term memory of navigation habits. To verify the validity of the method, two-dimensional simulation experiments and three-dimensional simulation robot experiments were designed in this paper. The experimental results showed that the method presented in this paper not only surpassed other algorithms in terms of navigation efficiency and convergence speed, but also exhibited good adaptability to dynamic navigation tasks. Furthermore, our method can be effectively applied to mobile robots.

Variable Time-Step Physics Engine with Continuous Compliance Contact Model for Optimal Robotic Grinding Trajectory Planning.

In the transition from virtual environments to real-world applications, the role of physics engines is crucial for accurately emulating and representing systems. To address the prevalent issue of inaccurate simulations, this paper introduces a novel physics engine uniquely designed with a compliant contact model designed for robotic grinding. It features continuous and variable time-step simulations, emphasizing accurate contact force calculations during object collision. Firstly, the engine derives dynamic equations considering spring stiffness, damping coefficients, coefficients of restitution, and external forces. This facilitates the effective determination of dynamic parameters such as contact force, acceleration, velocity, and position throughout penetration processes continuously. Secondly, the approach utilizes effective inertia in developing the contact model, which is designed for multi-jointed robots through pose transformation. The proposed physics engine effectively captures energy conversion in scenarios with convex contact surface shapes through the application of spring dampers during collisions. Finally, the reliability of the contact solver in the simulation was verified through bouncing ball experiments and robotic grinding experiments under different coefficients of restitution. These experiments effectively recorded the continuous variations in parameters, such as contact force, verifying the integral stability of the system. In summary, this article advances physics engine technology beyond current geometrically constrained contact solutions, enhancing the accuracy of simulations and modeling in virtual environments. This is particularly significant in scenarios wherein there are constant changes in the outside world, such as robotic grinding tasks.

Autonomous gait switching method and experiments of a hexapod walking robot for Mars environment with multiple terrains

Autonomous gait switching method and experiments of a hexapod walking robot for Mars environment with multiple terrains

3. OBJECTIVE ANALYSIS AND COMPARISON OF STRESS LEVEL DURING ROBOTIC AND CONVENTIONAL LAPAROSCOPIC SURGERY

Abstract Introduction The aim of this study is to objectively analyse the stress experienced by surgeons during the performance of training tasks in robotic and conventional laparoscopic surgery. Methods For the parameters related to stress level, an electrodermal activity sensor (EDA) from the company Movisens was used. This sensor is placed on the subject's ankle using two electrodes that measure the impedance of the skin in the area in order to evaluate the surgeon's level of alertness in micro-siemens (µS). The surgical robot used was the Versius Platform from CMR Surgical. A group of 4 surgeons with different levels of experience (novice or expert) were evaluated under a series of 7 tasks on a physical laparoscopic simulator, with tasks progressively increasing in difficulty. Results Regarding the difference between the two surgical modalities, significantly lower values were obtained when using the robotic platform, with the difference being more pronounced among the less experienced surgeons. In the instance of the expert with prior surgical robotics experience, stress levels were found to be 100 times lower compared to the expert solely experienced in conventional laparoscopy. Conclusion The results show that not only do subjects with superior experience in robotic surgery exhibit lower stress levels, but that novices maintain lower stress levels than experts in conventional laparoscopic surgery due to their lack of predisposition towards a specific surgical discipline.

An End-to-End Inclination State Monitoring Method for Collaborative Robotic Drilling Based on Resnet Neural Network.

The collaborative robot can complete various drilling tasks in complex processing environments thanks to the high flexibility, small size and high load ratio. However, the inherent weaknesses of low rigidity and variable rigidity in robots bring detrimental effects to surface quality and drilling efficiency. Effective online monitoring of the drilling quality is critical to achieve high performance robotic drilling. To this end, an end-to-end drilling-state monitoring framework is developed in this paper, where the drilling quality can be monitored through online-measured vibration signals. To evaluate the drilling effect, a Canny operator-based edge detection method is used to quantify the inclination state of robotic drilling, which provides the data labeling information. Then, a robotic drilling inclination state monitoring model is constructed based on the Resnet network to classify the drilling inclination states. With the aid of the training dataset labeled by different inclination states and the end-to-end training process, the relationship between the inclination states and vibration signals can be established. Finally, the proposed method is verified by collaborative robotic drilling experiments with different workpiece materials. The results show that the proposed method can effectively recognize the drilling inclination state with high accuracy for different workpiece materials, which demonstrates the effectiveness and applicability of this method.

Exploring the eating experience of a pneumatically-driven edible robot: Perception, taste, and texture.

This study investigated the effects of animated food consumption on human psychology. We developed a movable, edible robot and evaluated the participants' impressions induced by the visualization of its movements and eating of the robot. Although several types of edible robots have been developed, to the best of our knowledge, the psychological effects associated with the eating of a robot have not been investigated. We developed a pneumatically driven edible robot using gelatin and sugar. We examined its perceived appearance and the participants' impressions when it was eaten. In the robot-eating experiment, we evaluated two conditions: one in which the robot was moved and one in which it was stationary. Our results showed that participants perceived the moving robot differently from the stationary robot, leading to varied perceptions, when consuming it. Additionally, we observed a difference in perceived texture when the robot was bitten and chewed under the two conditions. These findings provide valuable insights into the practical applications of edible robots in various contexts, such as the medical field and culinary entertainment.

Innovative Maritime Uncrewed Systems and Satellite Solutions for Shallow Water Bathymetric Assessment

Shallow water bathymetry is a topic of significant interest in various fields, including civil construction, port monitoring, and military operations. This study presents several methods for assessing shallow water bathymetry using maritime uncrewed systems (MUSs) integrated with advanced and innovative sensors such as Light Detection and Ranging (LiDAR) and multibeam echosounder (MBES). Furthermore, this study comprehensively describes satellite-derived bathymetry (SDB) techniques within the same geographical area. Each technique is thoroughly outlined with respect to its implementation and resultant data, followed by an analytical comparison encompassing their accuracy, precision, rapidness, and operational efficiency. The accuracy and precision of the methods were evaluated using a bathymetric reference survey conducted with traditional means, prior to the MUS survey and with cross-comparisons between all the approaches. In each assessment of the survey methodologies, a comprehensive evaluation is conducted, explaining both the advantages and limitations for each approach, thereby enabling an inclusive understanding for the reader regarding the efficacy and applicability of these methods. The experiments were conducted as part of the Robotic Experimentation and Prototyping using Maritime Unmanned Systems 23 (REPMUS23) multinational exercise, which was part of the Rapid Environmental Assessment (REA) experimentations.

A Comprehensive Review of the Current State of Robot-assisted Laparoscopic Salvage Prostatectomy.

Salvage robot assisted radical prostatectomy (sRARP) is performed for patients with biochemical or biopsy proven, localized prostate cancer recurrences after radiation or ablative therapies. Traditionally, sRARP has been avoided by lower volume surgeons due to technical demand and high complication rates. Post-radiation sRARP outcomes studies exist but remain few in number. With increasing use of whole gland and focal ablative therapies, updates on sRARP in this setting are needed. The aim of this narrative review is to provide an overview of recently reviewed studies on the oncologic outcomes, functional outcomes, and complications after post-radiation and post-ablative sRARP. Tips and tricks are provided to guide surgeons who may perform sRARP. We performed a non-systematic literature search of PubMed and MEDLINE for the most relevant articles pertaining to the outlined topics from 2010-2022 without limitation on study design. Only case reports, editorial comments, letters, and manuscripts in non-English languages were excluded. Key Content and Findings: Salvage robotic radical prostatectomy is performed in cases of biochemical recurrence after radiation or ablative therapies. Oncologic outcomes after sRARP are worse compared to primary surgery (pRARP) though improvements have been made with the robotic approach when compared to open salvage prostatectomy. Higher pre-sRARP PSA levels and more advanced pathologic stage portend worse oncologic outcomes. Patients meeting low-risk, EAU-biochemical recurrence criteria have improved oncologic outcomes compared to those with high-risk BCR. While complication rates in sRARP are higher compared to pRARP, Retzius sparing approaches may reduce complication rates, particularly rectal injuries. In comparison to the traditional open approach, sRARP is associated with a lower rate of bladder neck contracture. In terms of functional outcomes, potency rates after sRARP are poor and continence rates are low, though Retzius sparing approaches demonstrate acceptable recovery of urinary continence by 1 year, post-operatively. Advances in the robotic platform and improvement in robotic experience have resulted in acceptable complication rates after sRARP. However, oncologic and functional outcomes after sRARP in both the post-radiation and post-ablation settings are worse compared to pRARP. Thus, when engaging in shared decision making with patients regarding the initial management of localized prostate cancer, patients should be educated regarding oncologic and functional outcomes and complications in the case of biochemically recurrent prostate cancer that may require sRARP.

TRENDS IN STUDIES ON EDUCATIONAL ROBOTICS IN RECENT YEARS: A CONTENT ANALYSIS

This study aims to analyze the general trends in educational robotics in recent years. The document analysis method was preferred in this research. Accordingly, the content of 100 articles published in the field of educational robotics in recent years was analyzed in terms of methodology, sample characteristics, robotic tools used, commonly covered topics, and findings. The study revealed that Lego Mindstorms, Arduino, Python, and Scratch are the most commonly used robotic tools in educational robotics. It was observed that students predominantly participated in the studies, with a focus on university and middle school levels. Moreover, the analysis indicated that quantitative studies mostly employed scales and questionnaires, whereas qualitative studies frequently used interviews and observation forms as measurement tools. The most frequently examined variables in these studies were computational thinking, problem-solving, creativity, academic achievement, collaboration, and motivation. While the findings generally emphasized the positive effects of educational robotics and its significance for integration into other domains, the challenges of educators’ knowledge and experience in robotics, infrastructure and technical issues, and difficulties in practical implementation were also identified as disadvantages.

Learning dexterity from human hand motion in internet videos

To build general robotic agents that can operate in many environments, it is often useful for robots to collect experience in the real world. However, unguided experience collection is often not feasible due to safety, time, and hardware restrictions. We thus propose leveraging the next best thing as real world experience: videos of humans using their hands. To utilize these videos, we develop a method that retargets any 1st person or 3rd person video of human hands and arms into the robot hand and arm trajectories. While retargeting is a difficult problem, our key insight is to rely on only internet human hand video to train it. We use this method to present results in two areas: First, we build a system that enables any human to control a robot hand and arm, simply by demonstrating motions with their own hand. The robot observes the human operator via a single RGB camera and imitates their actions in real-time. This enables the robot to collect real-world experience safely using supervision. See these results at https://robotic-telekinesis.github.io. Second, we retarget in-the-wild human internet video into task-conditioned pseudo-robot trajectories to use as artificial robot experience. This learning algorithm leverages action priors from human hand actions, visual features from the images, and physical priors from dynamical systems to pretrain typical human behavior for a particular robot task. We show that by leveraging internet human hand experience, we need fewer robot demonstrations compared to many other methods. See these results at https://video-dex.github.io

Reactive human–robot collaborative manipulation of deformable linear objects using a new topological latent control model

Real-time reactive manipulation of deformable linear objects is a challenging task that requires robots to quickly and adaptively respond to changes in the object’s deformed shape that result from external forces. In this paper, a novel approach is proposed for real-time reactive deformable linear object manipulation in the context of human–robot collaboration. The proposed approach combines a topological latent representation and a fixed-time sliding mode controller to enable seamless interaction between humans and robots. The introduced topological control model offers a framework for controlling the dynamic shape of deformable objects. By leveraging the topological representation, our approach captures the connectivity and structure of the objects’ shapes within a latent space. This enables improved generalization and performance when handling complex deformable shapes. A fixed-time sliding mode controller ensures that the object is manipulated in real-time, while also ensuring that it remains accurate and stable during the manipulation process. To validate our proposed framework, we first conduct motor-robot experiments to simulate fixed human interaction processes, enabling straightforward comparisons with other approaches. We then follow up with human–robot experiments to demonstrate the effectiveness of our approach.

Self-organized swarm robot for multi-target trapping based on self-regulated density interaction

In swarm robotics, multi-target trapping usually relies on global information or explicit communication, posing a challenge for robots to autonomously self-organize and trap multiple targets with only local perceptual data. We present a self-regulated density-based approach for self-organized multi-target trapping. This method employs density-based negative feedback control and density-distance weighted target selection. Our approach leverages distributed perception, where robots perceive nearby peers within their sensory range, eliminating the need for explicit information exchange and enabling autonomous trapping. During task execution, negative feedback control adjusts swarm density, ensuring it reaches an optimal level. Building on this, individual robots use density fields and relative target positions to select suitable targets, achieving self-regulated dispersed selection and multi-target trapping. We validate our approach through numerical simulations and real-world robot experiments. Results demonstrate stable self-organization, efficient self-regulated dispersed target selection, and successful target trapping, even with a limited number of trapping robots in low-redundancy scenarios.

Agreebot introduction dialogue in human–robot interaction: improving the acceptability of robot statements on incapable robotic experiences

Dialogue robots have gained widespread use in various domains. However, their ability to engage in conversations about actions that robots cannot perform, such as eating and traveling, remains a challenge. This study proposes a dialogue strategy called Agreebot Introduction Dialogue to enhance the acceptability of robot statements related to actions that robots are incapable of performing. This strategy involved referring to the presence of another robot with the same opinion. We examined our proposed method by conducting a dialogue experiment with 43 participants. The results indicated that the proposed dialogue strategy increased the acceptance of the robot's statements regarding food topics and improved user engagement behavior. This suggests that our strategy enables dialogue robots to participate effectively in diverse scenarios, including conversations on certain topics that are typically challenging for robots.

Location Reasoning of Target Objects based on Human Common Sense and Robot Experiences

Location Reasoning of Target Objects based on Human Common Sense and Robot Experiences

Distributed Robust Fault Estimation for Multiagent Systems Based on Transition Variable Estimator.

This article investigates the distributed robust fault estimation for a class of multiagent systems with actuator faults and nonlinear uncertainties. To estimate the actuator faults and system states simultaneously, a novel transition variable estimator is constructed. Compared with existing similar results, the fault estimator existing condition is not necessary for designing the transition variable estimator. Furthermore, the bounds of the faults and their derivatives can be unknown in designing the estimator for each agent in the system. The parameters of the estimator are calculated by using Schur decomposition and linear matrix inequality algorithm. Finally, the performance of the proposed method is demonstrated through experiments of wheeled mobile robots.

Task Space Compliant Control and Six-Dimensional Force Regulation Toward Automated Robotic Ultrasound Imaging

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> We propose a general control framework for task space compliant motion and six-dimensional (6-D) force regulation towards automated robotic ultrasound (US) imaging. The framework endows a position-controlled robotic manipulator with the capability of accurate compliant motion in free space and accurate force control in motion-constrained environment. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> An intuitive six degree-of-freedom (6-DoF) admittance control model expressed in an arbitrary Cartesian body frame is mathematically derived with closed-form task space error mapping. Its practical implementation on widely-used collaborative manipulators is proposed to achieve full task space compliant behaviors and accurate 6-D force control. A hybrid control law is presented to achieve good motion accuracy in free space and improved coupled stability in motion-constrained environment. The coupled model of physical human-robot interaction is established and the reason for the improved coupled stability is analyzed through simulation. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> Evaluation experiments on the proposed control framework were performed to show the effectiveness. The mean error of compliant trajectory following was less than 0.30 mm in free space. The mean relative force and moment control accuracy in three orthogonal directions was better than 0.5% and 0.8%, respectively. The improved coupled stability under the same model parameters was also confirmed by human-robot interaction experiments. Finally, an automated robotic US imaging experiment on a human volunteer in a real clinical scenario was carried out to show the potential application of our proposed framework. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> Experimental results have shown the advantages of the control framework, including satisfied force control accuracy, high accuracy of compliant motion, improved coupled stability, and system effectiveness on a human volunteer. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper was motivated by the increasing needs of automated ultrasound (US) scanning for both diagnostic and interventional purpose. Clinical sonographers suffer from repeated workload when performing diagnostic US imaging, which could benefit from automated robotic scanning. Robotic US imaging involves physical interaction between the robot end-effector (i.e., US transducer) and the human body. The dynamics of the interaction is regulated by the control law to guarantee the contact of the US transducer and the safety of the procedure. Most existing works have focused on regulating in-plane contact force in terms of the position without considering the compliance in other dimensions. However, it is not a trivial work to extend the positional compliance to six degree-of-freedom (6-DoF) compliance. As the prevalence of low-cost collaborative robotic arms in medical scenarios, how to perform 6-DoF compliant trajectory following and accurate six-dimensional (6-D) force control on these robotic arms becomes increasingly important. This paper gives a complete general solution to achieve 6-DoF compliant control and 6-D force regulation with accurate kinematics on a position-controlled robotic arm. A hybrid control law is proposed to switch the government of “instantaneous model” and “theoretical model” to achieve compliant motion accuracy in free space and improved coupled stability in motion-constrained environment. No expensive torque sensors and torque control interface are required. And no prior geometric knowledge about the scanning object is needed. We have demonstrated the application for robotic US imaging in a real clinical scenario.

Automation and machine learning augmented by large language models in a catalysis study.

Recent advancements in artificial intelligence and automation are transforming catalyst discovery and design from traditional trial-and-error manual mode into intelligent, high-throughput digital methodologies. This transformation is driven by four key components, including high-throughput information extraction, automated robotic experimentation, real-time feedback for iterative optimization, and interpretable machine learning for generating new knowledge. These innovations have given rise to the development of self-driving labs and significantly accelerated materials research. Over the past two years, the emergence of large language models (LLMs) has added a new dimension to this field, providing unprecedented flexibility in information integration, decision-making, and interacting with human researchers. This review explores how LLMs are reshaping catalyst design, heralding a revolutionary change in the fields.