Object Handovers: A Review for Robotics Ortenzi, Valerio; Cosgun, Akansel; Pardi, Tommaso ...
IEEE transactions on robotics,
2021-Dec., 2021-12-00, Letnik:
37, Številka:
6
Journal Article
Recenzirano
Odprti dostop
This article surveys the literature on human-robot object handovers. A handover is a collaborative joint action, where an agent, the giver, gives an object to another agent, the receiver. The ...physical exchange starts when the receiver first contacts the object held by the giver and ends when the giver fully releases the object to the receiver. However, important cognitive and physical processes begin before the physical exchange, including initiating implicit agreement with respect to the location and timing of the exchange. From this perspective, we structure our review into the two main phases delimited by the aforementioned events: a prehandover phase and the physical exchange. We focus our analysis on the two actors (giver and receiver) and report the state of the art of robotic givers (robot-to-human handovers) and the robotic receivers (human-to-robot handovers). We report a comprehensive list of qualitative and quantitative metrics commonly used to assess the interaction. While focusing our review on the cognitive level (e.g., prediction, perception, motion planning, and learning) and the physical level (e.g., motion, grasping, and grip release) of the handover, we also discuss safety. We compare the behaviors displayed during human-to-human handovers to the state of the art of robotic assistants and identify the major areas of improvement for robotic assistants to reach performance comparable to human interactions. Finally, we propose a minimal set of metrics that should be used in order to enable a fair comparison among the approaches.
Tactile sensing is a key sensor modality for robots interacting with their surroundings. These sensors provide a rich and diverse set of data signals that contain detailed information collected from ...contacts between the robot and its environment. The data are however not limited to individual contacts and can be used to extract a wide range of information about the objects in the environment as well as the actions of the robot during the interactions. In this article, we provide an overview of tactile information and its applications in robotics. We present a hierarchy consisting of raw, contact, object, and action levels to structure the tactile information, with higher-level information often building upon lower-level information. We discuss different types of information that can be extracted at each level of the hierarchy. The article also includes an overview of different types of robot applications and the types of tactile information that they employ. Finally we end the article with a discussion for future tactile applications which are still beyond the current capabilities of robots.
Robot assistants and professional coworkers are becoming a commodity in domestic and industrial settings. In order to enable robots to share their workspace with humans and physically interact with ...them, fast and reliable handling of possible collisions on the entire robot structure is needed, along with control strategies for safe robot reaction. The primary motivation is the prevention or limitation of possible human injury due to physical contacts. In this survey paper, based on our early work on the subject, we review, extend, compare, and evaluate experimentally model-based algorithms for real-time collision detection, isolation, and identification that use only proprioceptive sensors. This covers the context-independent phases of the collision event pipeline for robots interacting with the environment, as in physical human-robot interaction or manipulation tasks. The problem is addressed for rigid robots first and then extended to the presence of joint/transmission flexibility. The basic physically motivated solution has already been applied to numerous robotic systems worldwide, ranging from manipulators and humanoids to flying robots, and even to commercial products.
In this article, an admittance-based controller for physical human-robot interaction (pHRI) is presented to perform the coordinated operation in the constrained task space. An admittance model and a ...soft saturation function are employed to generate a differentiable reference trajectory to ensure that the end-effector motion of the manipulator complies with the human operation and avoids collision with surroundings. Then, an adaptive neural network (NN) controller involving integral barrier Lyapunov function (IBLF) is designed to deal with tracking issues. Meanwhile, the controller can guarantee the end-effector of the manipulator limited in the constrained task space. A learning method based on the radial basis function NN (RBFNN) is involved in controller design to compensate for the dynamic uncertainties and improve tracking performance. The IBLF method is provided to prevent violations of the constrained task space. We prove that all states of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB) by utilizing the Lyapunov stability principles. At last, the effectiveness of the proposed algorithm is verified on a Baxter robot experiment platform. Note to Practitioners -This work is motivated by the neglect of safety in existing controller design in physical human-robot interaction (pHRI), which exists in industry and services, such as assembly and medical care. It is considerably required in the controller design for rigorously handling constraints. Therefore, in this article, we propose a novel admittance-based human-robot interaction controller. The developed controller has the following functionalities: 1) ensuring reference trajectory remaining in the constrained task space: a differentiable reference trajectory is shaped by the desired admittance model and a soft saturation function; 2) solving uncertainties of robotic dynamics: a learning approach based on radial basis function neural network (RBFNN) is involved in controller design; and 3) ensuring the end-effector of the manipulator remaining in the constrained task space: different from other barrier Lyapunov function (BLF), integral BLF (IBLF) is proposed to constrain system output directly rather than tracking error, which may be more convenient for controller designers. The controller can be potentially applied in many areas. First, it can be used in the rehabilitation robot to avoid injuring the patient by limiting the motion. Second, it can ensure the end-effector of the industrial manipulator in a prescribed task region. In some industrial tasks, dangerous or damageable tools are mounted on the end-effector, and it will hurt humans and bring damage to the robot when the end-effector is out of the prescribed task region. Third, it may bring a new idea to the designed controller for avoiding collisions in pHRI when collisions occur in the prescribed trajectory of end-effector.
Advances in deep learning over the last decade have led to a flurry of research in the application of deep artificial neural networks to robotic systems, with at least 30 papers published on the ...subject between 2014 and the present. This review discusses the applications, benefits, and limitations of deep learning vis-à-vis physical robotic systems, using contemporary research as exemplars. It is intended to communicate recent advances to the wider robotics community and inspire additional interest in and application of deep learning in robotics.
Currently, a large number of industrial robots have been deployed to replace or assist humans to perform various repetitive and dangerous manufacturing tasks. However, based on current technological ...capabilities, such robotics field is rapidly evolving so that humans are not only sharing the same workspace with robots, but also are using robots as useful assistants. Consequently, due to this new type of emerging robotic systems, industrial collaborative robots or cobots, human and robot co-workers have been able to work side-by-side as collaborators to accomplish tasks in industrial environments. Therefore, new human-robot interaction systems have been developed for such systems to be able to utilize the capabilities of both humans and robots. Accordingly, this article presents a literature review of major recent works on human-robot interactions in industrial collaborative robots, conducted during the last decade (between 2008 and 2017). Additionally, the article proposes a tentative classification of the content of these works into several categories and sub-categories. Finally, this paper addresses some challenges of industrial collaborative robotics and explores future research issues.
MEDER 2018, the IFToMM International Symposium on Mechanism Design for Robotics, was the fourth event in a series that was started in 2010 as a specific conference activity on mechanisms for robots. ...The aim of the MEDER Symposium is to bring researchers, industry professionals, and students together from a broad range of disciplines dealing with mechanisms for robots, in an intimate, collegial, and stimulating environment. In the 2018 MEDER event, we received significant attention regarding this initiative, as can be seen by the fact that the Proceedings contain contributions by authors from all around the world.The Proceedings of the MEDER 2018 Symposium have been published within the Springer book series on MMS, and the book contains 52 papers that have been selected after review for oral presentation. These papers cover several aspects of the wide field of robotics dealing with mechanism aspects in theory, design, numerical evaluations, and applications.This Special Issue of Robotics (https://www.mdpi.com/journal/robotics/special_issues/MDR) has been obtained as a result of a second review process and selection, but all the papers that have been accepted for MEDER 2018 are of very good quality with interesting contents that are suitable for journal publication, and the selection process has been difficult.
In this work, a human motion intention prediction method based on an autoregressive (AR) model for teleoperation is developed. Based on this method, the robot's motion trajectory can be updated in ...real time through updating the parameters of the AR model. In the teleoperated robot's control loop, a virtual force model is defined to describe the interaction profile and to correct the robot's motion trajectory in real time. The proposed human motion prediction algorithm acts as a feedforward model to update the robot's motion and to revise this motion in the process of human-robot interaction (HRI). The convergence of this method is analyzed theoretically. Comparative studies demonstrate the enhanced performance of the proposed approach. Note to Practitioners-In general, the robot trajectory is predetermined and it does not consider the influence of the interaction profiles in terms of position and interaction force between the human and the robot. In addition, it is hard to quantify the influence of interaction profile for the robot trajectory. For teleoperation, an AR-based model is proposed to predict the trajectory of the human and then to update the trajectory of the robot. The developed method includes the following aspects: 1) the robot trajectory can be regulated based on the interaction profiles; 2) the feedforward model can estimate the trajectory of the human to achieve the purpose of human intention recognition in advance for the robot; and 3) the proposed method can be potentially utilized for telerehabilitation, microsurgery, and so on.
Safety is a fundamental prerequisite that must be addressed before any interaction of robots with humans. Safety has been generally understood and studied as the physical safety of robots in ...human–robot interaction, whereas how humans perceive these robots has received less attention. Physical safety is a necessary condition for safe human–robot interaction. However, it is not a sufficient condition. A robot that is safe by hardware and software design can still be perceived as unsafe. This article focuses on perceived safety in human–robot interaction. We identified six factors that are closely related to perceived safety based on the literature and the insights obtained from our user studies. The identified factors are the context of robot use, comfort, experience and familiarity with robots, trust, the sense of control over the interaction, and transparent and predictable robot actions. We then made a literature review to identify the robot-related factors that influence perceived safety. Based the literature, we propose a taxonomy which includes human-related and robot-related factors. These factors can help researchers to quantify perceived safety of humans during their interactions with robots. The quantification of perceived safety can yield computational models that would allow mitigating psychological harm.
Hotel industry started to adopt service robots, which are considered a future workforce. However, no attempt was conducted to examine the dimensionality of service quality of service robots. This ...paper aims to understand the influence of human-robot interaction from the viewpoint of hoteliers and guests. Two studies are conducted in this respect. Study 1 organizes focus-group interviews with hotel managers from various departments to elicit themes related to guest-robot interaction and robot-delivered services. Based on the findings in Study 1, Study 2 conducts an experiment to examine and compare hotel guests' perceptions about the quality of services provided by human staff and service robots, as well as their joint services. Human staff services are perceived higher than the services of service robots in terms of interaction quality and physical service environment. However, no significant difference in outcome quality is noted.
PDF
