Motion capture and AR based programming by demonstration for industrial robots using handheld teaching device.

In the industrial robots field, efficient and convenient programming methods have been a hot research topic. In recent years, immersive simulation technology has been developing rapidly in many fields, which provides new horizons for the development of industrial robots. This paper presents a HTC VIVE laser scan motion capture and Holohens augmented reality (AR) based interactive Programming by Demonstration (PbD) system for industrial robot. A portable Handheld Teaching Device (HTD) and its calibration algorithm are designed in the system. The portable HTD which is tracked by a laser motion capture system can be viewed as an AR robot end-effector to teach paths. Meanwhile, the AR robot can be simulated in real time during programing. In addition, the robot reproducing the operator's actions at the same position in space is the focus of programming. So, Multi-system registration methods are proposed to determine the relationship between robot systems, motion capture systems and virtual robot systems. Meanwhile, a path planning algorithm is proposed to convert the captured raw path points into robot-executable code. For unskilled operators, they can easily perform complex programming using the HTD. For skilled senior workers, their skills can be quickly learned by robots using the system.

In Vivo Feasibility Study: Evaluating Autonomous Data-Driven Robotic Needle Trajectory Correction in MRI-Guided Transperineal Procedures.

This study addresses the targeting challenges in MRI-guided transperineal needle placement for prostate cancer (PCa) diagnosis and treatment, a procedure where accuracy is crucial for effective outcomes. We introduce a parameter-agnostic trajectory correction approach incorporating a data-driven closed-loop strategy by radial displacement and an FBG-based shape sensing to enable autonomous needle steering. In an animal study designed to emulate clinical complexity and assess MRI compatibility through a PCa mock biopsy procedure, our approach demonstrated a significant improvement in targeting accuracy (p<0.05), with mean target error of only 2.2 ± 1.9 mm on first insertion attempts, without needle reinsertions. To the best of our knowledge, this work represents the first in vivo evaluation of robotic needle steering with FBG-sensor feedback, marking a significant step towards its clinical translation.

The Use of Remote Presence Robotic Tele-Presentation in Rural and Remote Canada: A Systematic Review.

Objective: One potential solution to limited health care in rural and remote regions is remote presence robotic tele-presentation to allow health care providers to care for patients in their home community via a robotic interface. We synthesized evidence regarding the use of remote presence robotic tele-presentation in rural and/or remote Canadian health settings. Methods: Medline, PubMed, and Embase were searched up to August 2023. Remote presence robotic tele-presentation refers to any robotic device used for the purpose of presenting and/or collecting patient information. Primary research was included if the patient was located in remote and/or rural Canada, featured remote presence robotic tele-presentation, and assessed patient, family, or clinician satisfaction, patient transport to nearby regional or urban center, health care costs, clinical outcomes, infrastructure outcomes, adverse events, or telementoring. Results: Six studies were included. Patients, nurses, and physicians all reported high levels of satisfaction when using the remote presence robotic tele-presentation. Fifty to sixty-three percent of patients were managed in their home community and did not require transfer to another center. Remote presence robotic sonography resulted in adequate imaging in 81% of first trimester ultrasound limited exams but was less useful for second trimester complete obstetric ultrasounds (20% adequate imaging). Two of eight laparoscopic colorectal surgeries had to be converted to open surgeries. Telerobotic ultrasound clinics resulted in a diagnosis in 70% of cases. Conclusions: Evidence suggests remote presence robotic tele-presentation is a safe and cost-effective approach to providing care in distant communities and can prevent some transfers and evacuations to tertiary hospitals.

Are nurses and patients willing to work with service robots in healthcare? A mixed-methods study.

INTRODUCTION: Scholars have become increasingly interested in incorporating robots into healthcare. While there is a growing body of research examining nurses' and patients' attitudes towards using robots in healthcare, no prior research has specifically explored their willingness to integrate service robots within the Egyptian healthcare context. AIM: The aim of this study was twofold: (a) to explore the behavioral intentions of nurses to accept robots in their workplace, and (b) to examine the willingness of patients to use service robots in healthcare settings. METHODS: A mixed-methods study was conducted. Quantitative data were collected from 301 nurses using the Behavioral Intention to Accept Robots in the Workplace Scale and from 467 patients using the Service Robot Integration Willingness Scale through convenience sampling at three tertiary public hospitals in Port Said, Egypt. Qualitative data were obtained through in-depth, semi-structured interviews with 16 nurses, focusing on their perspectives and concerns regarding robot integration. Descriptive analyses were used to analyze quantitative data, and thematic analysis was used to analyze qualitative data. RESULTS: Quantitative results indicated a moderate level of behavioral intention to use robots among nurses. Patients demonstrated low willingness to use service robots. In the qualitative analysis of the data obtained from the interviews with nurses, three categories (Concerns about Robots, Roles and Competencies, and Potential Benefits) and eight themes (interaction and emotions, maintenance and reliability, job insecurity, role clarity, competence in critical care, trustworthiness, reducing physical strain, and specialized applications) were identified. CONCLUSION: The results of this study indicate that nurses' behavioral intention to accept service robots in healthcare settings is moderate and their acceptance is influenced by various factors related to their concerns about robots, roles and competencies, and potential benefits they could gain. Patients showed a low level of willingness to use service robots in healthcare settings. IMPLICATION: Providing targeted educational programs to nurses and patients, assuring them with the provision of robust maintenance protocols, enhancing their confidence in the capabilities of robots, and defining clear roles for robots are crucial for the successful integration of robots into healthcare settings.

ExTraCT - Explainable trajectory corrections for language-based human-robot interaction using textual feature descriptions.

Introduction: In human-robot interaction (HRI), understanding human intent is crucial for robots to perform tasks that align with user preferences. Traditional methods that aim to modify robot trajectories based on language corrections often require extensive training to generalize across diverse objects, initial trajectories, and scenarios. This work presents ExTraCT, a modular framework designed to modify robot trajectories (and behaviour) using natural language input. Methods: Unlike traditional end-to-end learning approaches, ExTraCT separates language understanding from trajectory modification, allowing robots to adapt language corrections to new tasks-including those with complex motions like scooping-as well as various initial trajectories and object configurations without additional end-to-end training. ExTraCT leverages Large Language Models (LLMs) to semantically match language corrections to predefined trajectory modification functions, allowing the robot to make necessary adjustments to its path. This modular approach overcomes the limitations of pre-trained datasets and offers versatility across various applications. Results: Comprehensive user studies conducted in simulation and with a physical robot arm demonstrated that ExTraCT's trajectory corrections are more accurate and preferred by users in 80% of cases compared to the baseline. Discussion: ExTraCT offers a more explainable approach to understanding language corrections, which could facilitate learning human preferences. We also demonstrated the adaptability and effectiveness of ExTraCT in a complex scenarios like assistive feeding, presenting it as a versatile solution across various HRI applications.

Review: Application of 3D Printing Technology in Soft Robots.

Soft robots, inspired by living organisms in nature, are primarily made of soft materials, and can be used to perform delicate tasks due to their high flexibility, such as grasping and locomotion. However, it is a challenge to efficiently manufacture soft robots with complex functions. In recent years, 3D printing technology has greatly improved the efficiency and flexibility of manufacturing soft robots. Unlike traditional subtractive manufacturing technologies, 3D printing, as an additive manufacturing method, can directly produce parts of high quality and complex geometry for soft robots without manual errors or costly post-processing. In this review, we investigate the basic concepts and working principles of current 3D printing technologies, including stereolithography, selective laser sintering, material extrusion, and material jetting. The advantages and disadvantages of fabricating soft robots are discussed. Various 3D printing materials for soft robots are introduced, including elastomers, shape memory polymers, hydrogels, composites, and other materials. Their functions and limitations in soft robots are illustrated. The existing 3D-printed soft robots, including soft grippers, soft locomotion robots, and wearable soft robots, are demonstrated. Their application in industrial, manufacturing, service, and assistive medical fields is discussed. We summarize the challenges of 3D printing at the technical level, material level, and application level. The prospects of 3D printing technology in the field of soft robots are explored.

People's judgments of humans and robots in a classic moral dilemma.

How do ordinary people evaluate robots that make morally significant decisions? Previous work has found both equal and different evaluations, and different ones in either direction. In 13 studies (N = 7670), we asked people to evaluate humans and robots that make decisions in norm conflicts (variants of the classic trolley dilemma). We examined several conditions that may influence whether moral evaluations of human and robot agents are the same or different: the type of moral judgment (norms vs. blame); the structure of the dilemma (side effect vs. means-end); salience of particular information (victim, outcome); culture (Japan vs. US); and encouraged empathy. Norms for humans and robots are broadly similar, but blame judgments show a robust asymmetry under one condition: Humans are blamed less than robots specifically for inaction decisions-here, refraining from sacrificing one person for the good of many. This asymmetry may emerge because people appreciate that the human faces an impossible decision and deserves mitigated blame for inaction; when evaluating a robot, such appreciation appears to be lacking. However, our evidence for this explanation is mixed. We discuss alternative explanations and offer methodological guidance for future work into people's moral judgment of robots and humans.

Reconfigurable origami with variable stiffness joints for adaptive robotic locomotion and grasping.

With its compactness and foldability, origami has recently been applied to robotic systems to enable versatile robots and mechanisms while maintaining a low weight and compact form. This work investigates how to generate different motions and shapes for origami by tuning its creases' stiffness on the fly. The stiffness tuning is realized by a composite material made by sandwiching a thermoplastic layer between two shape memory polymer layers. This enables the composite to act as a living hinge, whose stiffness can be actively controlled through Joule heating. To demonstrate our concept, we fabricate an origami module with four variable stiffness joints (VSJs), allowing it to have freely controlled crease stiffnesses across its surface. We characterize the origami module's versatile motion when heating different VSJs with different temperatures. We further use two origami modules to build a two-legged robot that can locomote on the ground with different locomotion gaits. The same robot is also used as an adaptive gripper for grasping tasks. Our work can potentially enable more versatile robotic systems made from origami as well as other mechanical systems with programmable properties (e.g. mechanical metamaterials).This article is part of the theme issue 'Origami/Kirigami-inspired structures: from fundamentals to applications'.

Effects of using an active hand exoskeleton for drilling tasks: A pilot study.

INTRODUCTION: Several studies have assessed and validated the impact of exoskeletons on back and shoulder muscle activation; however, limited research has explored the role that exoskeletons could play in mitigating lower arm-related disorders. This study assessed the impact of Ironhand, an active hand exoskeleton (H-EXO) designed to reduce grip force exertion, on worker exertion levels using a two-phase experimental design. METHOD: Ten male participants performed a controlled, simulated drilling activity, while three male participants completed an uncontrolled concrete demolition activity. The impact of the exoskeleton was assessed in terms of muscle activity across three different muscles using electromyography (EMG), perceived exertion, and perceived effectiveness. RESULTS: Results indicate that peak muscle activation decreased across the target muscle group when the H-EXO was used, with the greatest reduction (27%) observed in the Extensor Carpi Radialis (ECR). Using the exoskeleton in controlled conditions did not significantly influence perceived exertion levels. Users indicated that the H-EXO was a valuable technology and expressed willingness to use it for future tasks. PRACTICAL APPLICATIONS: This study showcases how glove-based exoskeletons can potentially reduce wrist-related disorders, thereby improving safety and productivity among workers. Future work should assess the impact of the H-EXO in various tasks, different work environments and configurations, and among diverse user groups.

W-VSLAM: A Visual Mapping Algorithm for Indoor Inspection Robots.

In recent years, with the widespread application of indoor inspection robots, high-precision, robust environmental perception has become essential for robotic mapping. Addressing the issues of visual-inertial estimation inaccuracies due to redundant pose degrees of freedom and accelerometer drift during the planar motion of mobile robots in indoor environments, we propose a visual SLAM perception method that integrates wheel odometry information. First, the robot's body pose is parameterized in SE(2) and the corresponding camera pose is parameterized in SE(3). On this basis, we derive the visual constraint residuals and their Jacobian matrices for reprojection observations using the camera projection model. We employ the concept of pre-integration to derive pose-constraint residuals and their Jacobian matrices and utilize marginalization theory to derive the relative pose residuals and their Jacobians for loop closure constraints. This approach solves the nonlinear optimization problem to obtain the optimal pose and landmark points of the ground-moving robot. A comparison with the ORBSLAM3 algorithm reveals that, in the recorded indoor environment datasets, the proposed algorithm demonstrates significantly higher perception accuracy, with root mean square error (RMSE) improvements of 89.2% in translation and 98.5% in rotation for absolute trajectory error (ATE). The overall trajectory localization accuracy ranges between 5 and 17 cm, validating the effectiveness of the proposed algorithm. These findings can be applied to preliminary mapping for the autonomous navigation of indoor mobile robots and serve as a basis for path planning based on the mapping results.

Implementing Autonomous Control in the Digital-Twins-Based Internet of Robotic Things for Remote Patient Monitoring.

Conventional patient monitoring methods require skin-to-skin contact, continuous observation, and long working shifts, causing physical and mental stress for medical professionals. Remote patient monitoring (RPM) assists healthcare workers in monitoring patients distantly using various wearable sensors, reducing stress and infection risk. RPM can be enabled by using the Digital Twins (DTs)-based Internet of Robotic Things (IoRT) that merges robotics with the Internet of Things (IoT) and creates a virtual twin (VT) that acquires sensor data from the physical twin (PT) during operation to reflect its behavior. However, manual navigation of PT causes cognitive fatigue for the operator, affecting trust dynamics, satisfaction, and task performance. Also, operating manual systems requires proper training and long-term experience. This research implements autonomous control in the DTs-based IoRT to remotely monitor patients with chronic or contagious diseases. This work extends our previous paper that required the user to manually operate the PT using its VT to collect patient data for medical inspection. The proposed decision-making algorithm enables the PT to autonomously navigate towards the patient's room, collect and transmit health data, and return to the base station while avoiding various obstacles. Rather than manually navigating, the medical personnel direct the PT to a specific target position using the Menu buttons. The medical staff can monitor the PT and the received sensor information in the pre-built virtual environment (VE). Based on the operator's preference, manual control of the PT is also achievable. The experimental outcomes and comparative analysis verify the efficiency of the proposed system.

German sugar beet farmers' intention to use autonomous field robots for seeding and weeding.

Robotic weed control is not yet widely adopted, despite its technological availability and proven economics and sustainability in crop cultivation by replacing seasonal labor and synthetic pesticides. This impedes technologically enabled changes toward more sustainable agricultural systems. Given that adopting robotics for the weeding process requires changing existing systems, farmers' appraisals for the new and the current weeding technology may constitute barriers. However, this dualism has been largely ignored by previous studies. Based on a duality approach, we investigate farmers' beliefs, and adaptive and maladaptive appraisals of current and new robotic weeding in sugar beets. The main variable of interest is their behavioral intention to adopt weeding robots. For our sample of German farmers, we identify the main enablers perceived efficacy of the robots and social norms. The main barrier are maladaptive rewards from traditional weeding. We recommend policy incentives to promote large-scale uptake of new and more sustainable robotic technologies. To improve efficacy perceptions of such robotic systems public demonstrations/talks are mostly relevant. Maladaptive rewards can be reduced, for instance, by notifying about the dependency of the current practices on future availability of synthetic inputs or seasonal workers.

Robot-Mediated Nudges for Workplace Health: Not a One-Size-Fits-All Modeling Problem.

Prolonged sedentary behavior in the vast population of office and remote workers leads to increased cardiovascular and musculoskeletal health challenges, and existing solutions for encouraging breaks are either costly health coaches or notification systems that are easily ignored. A socially assistive robot (SAR) for promoting healthy workplace practices could provide the physical presence of a health coach along with the scalability of a notification system. To investigate the impact of such a system, we implemented a SAR as an alternative break-taking support solution and examined its impact on individual users' break-taking habits over relatively long-term deployments. We conducted an initial two-month-long study (N = 7) to begin to understand the robot's influence beyond the point of novelty, and we followed up with a week-long data collection (N = 14) to augment the dataset size. The resulting data was used to inform a robot behavior model and formulate possible methods of personalizing robot behaviors. We found that uninterrupted sitting time tended to decrease with our SAR intervention. During model formulation, we found participant responsiveness to the break-taking prompts could be classified into three archetypes and that archetype-specific adjustments to the general model led to improved system success. These results indicate that break-taking prompts are not a one-size-fits-all problem, and that even a small dataset can support model personalization for improving the success of assistive robotic systems.

Social robots as skilled ignorant peers for supporting learning.

When designing social robots for educational settings, there is often an emphasis on domain knowledge. This presents challenges: 1) Either robots must autonomously acquire domain knowledge, a currently unsolved problem in HRI, or 2) the designers provide this knowledge implying re-programming the robot for new contexts. Recent research explores alternative, relatively easier to port, knowledge areas like student rapport, engagement, and synchrony though these constructs are typically treated as the ultimate goals, when the final goal should be students' learning. Our aim is to propose a shift in how engagement is considered, aligning it naturally with learning. We introduce the notion of a skilled ignorant peer robot: a robot peer that has little to no domain knowledge but possesses knowledge of student behaviours conducive to learning, i.e., behaviours indicative of productive engagement as extracted from student behavioral profiles. We formally investigate how such a robot's interventions manipulate the children's engagement conducive to learning. Specifically, we evaluate two versions of the proposed robot, namely, Harry and Hermione, in a user study with 136 students where each version differs in terms of the intervention strategy. Harry focuses on which suggestions to intervene with from a pool of communication, exploration, and reflection inducing suggestions, while Hermione also carefully considers when and why to intervene. While the teams interacting with Harry have higher productive engagement correlated to learning, this engagement is not affected by the robot's intervention scheme. In contrast, Hermione's well-timed interventions, deemed more useful, correlate with productive engagement though engagement is not correlated to learning. These results highlight the potential of a social educational robot as a skilled ignorant peer and stress the importance of precisely timing the robot interventions in a learning environment to be able to manipulate moderating variable of interest such as productive engagement.

External Steering of Vine Robots via Magnetic Actuation.

This article explores the concept of external magnetic control for vine robots to enable their high curvature steering and navigation for use in endoluminal applications. Vine robots, inspired by natural growth and locomotion strategies, present unique shape adaptation capabilities that allow passive deformation around obstacles. However, without additional steering mechanisms, they lack the ability to actively select the desired direction of growth. The principles of magnetically steered growing robots are discussed, and experimental results showcase the effectiveness of the proposed magnetic actuation approach. We present a 25-mm-diameter vine robot with an integrated magnetic tip capsule, including 6 degrees of freedom (DOF) localization system and camera, and demonstrate a minimum bending radius of 3.85 cm with an internal pressure of 30 kPa. Furthermore, we evaluate the robot's ability to form tight curvature through complex navigation tasks, with magnetic actuation allowing for extended free-space navigation without buckling. The suspension of the magnetic tip was also validated using the 6 DOF localization system to ensure that the shear-free nature of vine robots was preserved. Additionally, by exploiting the magnetic wrench at the tip, we showcase preliminary results of vine retraction. The findings contribute to the development of controllable vine robots for endoluminal applications, providing high tip force and shear-free navigation.

4D Printing Hybrid Soft Robots Enabled by Shape-Transformable Liquid Metal Nanoparticles.

In recent years, soft robotics has emerged as a rapidly expanding frontier research field that draws inspiration from the locomotion mechanisms of soft-bodied creatures in nature to achieve smooth and complex motion for diverse applications. However, the fabrication of soft robots with hybrid structures remains challenging due to limitations in material selection and the complex, multi-step processes involved in traditional manufacturing methods. Herein, a novel direct one-step additive manufacturing (3D printing) approach is introduced for the fabrication of hybrid robots composed of soft and rigid components for sophisticated tasks. Inspired by the shape-transformable liquid metal nanoparticles (LMNPs), a functional material toolkit with tuneable mechanical properties and deformability is developed by integrating differently shaped gallium-based nanoparticles (GNPs) into the 3D printing polymers. Then the direct printing of assembled or one-piece hybrid soft-rigid robots is presented through a single recipe of GNPs-integrated inks. This fabrication method enables precise control of the mechanical properties and shape memory properties within the hybrid structures of robot body with a customized structure design. Their capabilities are further demonstrated through the design and fabrication of hybrid robots as high-precision gripper, bioinspired motor, and hand rehabilitation device.

Design and Validation of a Novel Robotic Neck Brace for Cervical Traction.

Cervical traction is a common and effective treatment for degenerative disk diseases and pain in the cervical spine. However, the manual or mechanical methods of applying traction to the head-neck are limited due to variability in the applied forces and orientation of the head-neck relative to the shoulder during the procedure. Current robotic neck braces are not designed to provide independent rotation angles and independent vertical translation, or traction, to the brace end-effector connected to the head, making them unsuitable for traction application. This work proposes a novel architecture of a robotic neck brace, which can provide vertical traction to the head while keeping the head in a prescribed orientation, with flexion and lateral bending angles. In this paper, the kinematics of the end-effector attached to the head relative to a coordinate frame on the shoulders are described as well as the velocity kinematics and force control. The paper also describes benchtop experiments designed to validate the position control and the ability of the brace to provide a vertical traction force. It was shown that the maximum achievable end-effector orientations are 16° in flexion, 13.9° in extension, and ± 6.5° in lateral bending. The kinematic model of the active brace was validated using an independent motion capture system with a maximum root mean square error of 2.41°. In three different orientations of the end-effector, neutral, flexed, and laterally bent, the brace was able to provide a consistent upward traction force during intermittent force application. In these configurations, the force error has standard deviations of 0.55, 0.29, and 0.07N, respectively. This work validates the mechanism's ability to achieve a range of head orientations and provide consistent upward traction force in these orientations, making it a promising intervention tool in cases of cervical disk degeneration.

Robotic navigation with deep reinforcement learning in transthoracic echocardiography.

PURPOSE: The search for heart components in robotic transthoracic echocardiography is a time-consuming process. This paper proposes an optimized robotic navigation system for heart components using deep reinforcement learning to achieve an efficient and effective search technique for heart components. METHOD: The proposed method introduces (i) an optimized search behavior generation algorithm that avoids multiple local solutions and searches for the optimal solution and (ii) an optimized path generation algorithm that minimizes the search path, thereby realizing short search times. RESULTS: The mitral valve search with the proposed method reaches the optimal solution with a probability of 74.4%, the mitral valve confidence loss rate when the local solution stops is 16.3% on average, and the inspection time with the generated path is 48.6 s on average, which is 56.6% of the time cost of the conventional method. CONCLUSION: The results indicate that the proposed method improves the search efficiency, and the optimal location can be searched in many cases with the proposed method, and the loss rate of the confidence in the mitral valve was low even when a local solution rather than the optimal solution was reached. It is suggested that the proposed method enables accurate and quick robotic navigation to find heart components.

Flexible endoscope manipulating robot using quad-roller friction mechanism.

A robotic system for manipulating a flexible endoscope in surgery can provide enhanced accuracy and usability compared to manual operation. However, previous studies require large-scale, complex hardware systems to implement the rotational and translational motions of the soft endoscope cable. The conventional control of the endoscope by actuating the endoscope handle also leads to undesired slack between the endoscope tip and the handle, which becomes more problematic with long endoscopes such as a colonoscope. This study proposes a compact quad-roller friction mechanism that enables rotational and translational motions triggered not from the endoscope handle but at the endoscope tip. Controlling two pairs of tilted rollers achieves both types of motion within a small space. The proposed system also introduces an unsynchronized motion strategy between the handle and tip parts to minimize the robot's motion near the patient by employing the slack positively as a control index. Experiments indicate that the proposed system achieves accurate rotational and translational motions, and the unsynchronized control method reduces the total translational motion by up to 88% compared to the previous method.

Human perceptions of social robot deception behaviors: an exploratory analysis.

Introduction: Robots are being introduced into increasingly social environments. As these robots become more ingrained in social spaces, they will have to abide by the social norms that guide human interactions. At times, however, robots will violate norms and perhaps even deceive their human interaction partners. This study provides some of the first evidence for how people perceive and evaluate robot deception, especially three types of deception behaviors theorized in the technology ethics literature: External state deception (cues that intentionally misrepresent or omit details from the external world: e.g., lying), Hidden state deception (cues designed to conceal or obscure the presence of a capacity or internal state the robot possesses), and Superficial state deception (cues that suggest a robot has some capacity or internal state that it lacks). Methods: Participants (N = 498) were assigned to read one of three vignettes, each corresponding to one of the deceptive behavior types. Participants provided responses to qualitative and quantitative measures, which examined to what degree people approved of the behaviors, perceived them to be deceptive, found them to be justified, and believed that other agents were involved in the robots' deceptive behavior. Results: Participants rated hidden state deception as the most deceptive and approved of it the least among the three deception types. They considered external state and superficial state deception behaviors to be comparably deceptive; but while external state deception was generally approved, superficial state deception was not. Participants in the hidden state condition often implicated agents other than the robot in the deception. Conclusion: This study provides some of the first evidence for how people perceive and evaluate the deceptiveness of robot deception behavior types. This study found that people people distinguish among the three types of deception behaviors and see them as differently deceptive and approve of them differently. They also see at least the hidden state deception as stemming more from the designers than the robot itself.