IROS2019国际学术会议论文集An uation of Robot-to-Human Handover Configurations for Commercial Robots

An Evaluation of Robot-to-Human Handover Confi gurations for Commercial Robots Robin Rasch1, Sven Wachsmuth2and Matthias K onig1 AbstractThe handover of objects is a fundamental task in robot-human interaction. The nature of the handover can be varied by adjusting various parameters in order to achieve a human-like interaction. In this paper the fi nal handover confi guration is examined and analyzed. For this purpose, we conducted an experiment in which subjects taught a robot proper and improper end poses for different objects in different situations. We analyze the learned poses to determine what factors proper poses depend on. In addition to the poses, the confi gurations of the individual joints of the commercial robot are evaluated. Our results demonstrate that proper poses can be grouped into three clusters. These differ mainly in the rotation of the forearm. The height of a proper handover is also determined based on external factors. I. INTRODUCTION Over the past decade, the development of service robots has grown steadily. Unlike traditional industrial robots that operate in secure and locked environments, service robots interact with people. They can be utilized at home, in the offi ce or in other environments of daily life. Some applica- tions under development include homecare robots 1, service robots with smart home connections 2, and intelligent workplaces 3. As soon as a robot engages with a human being, there are interactions between the robot and the human being. An everyday interaction is the handover of objects from robots to humans and vice versa. Handing over objects is commonplace for people. Humans are able to successfully transfer different objects in different situations, such as stand- ing, sitting, and walking. While the handover is consciously executed, the details of the execution are perceived only subconsciously. Many studies make ad-hoc assumptions to increase the convenience and user acceptance of handovers. Various fac- tors and phases are considered. For example, Eguilu 4 examined the contact forces measured on a robot hand during the handover. The movement patterns that are passed through during a handover were also recorded, analyzed and proposed using models. While Shibata et al. 5 and Huber et al. 6 looked at models for handovers in a seated position, in our previous studies 7, 8 we analyzed handovers between two standing subjects and developed two different models. *This work is fi nancially supported by the German Federal Ministry of Education and Research (BMBF, Funding number: 03FH006PX5). 1Robin Rasch and Matthias K onig are with Faculty Campus Min- den, Bielefeld University of Applied Sciences, 32427 Minden, Germany firstname.surnamefh-bielefeld.de 2Sven Wachsmuth is with the Central Lab Facilities, Cognitive In- teraction Technology Excellence Cluster, Bielefeld University, Germany. swachsmutechfak.uni-bielefeld.de Fig. 1.Pepper is taught a proper (left) and a improper (right) example pose by human tutors. Both models can be confi gured and adapted by changing parameters. One of these parameters is the fi nal handover confi guration. This confi guration is composed of the pose of the end effector and the grasp type. The determination and generation of confi gurations are well-covered research areas. However, the most common approaches are designed for robot arms with many degrees of freedom, anthropo- morphous kinematics and several sensors. These approaches are diffi cult to adapt to robots that are limited by their hardware. Commercial robots today have these limitations in their kinematics and sensors. In our example, the Pepper robot by Aldeberan Robotics and SoftBank has an arm with only fi ve degrees of freedom, a small range of joint limits, a weak grip and an underactuated one-degree-of-freedom hand. This prevents some generated handover confi gurations (e.g., all confi gurations with dorsal extensions and radial or ulnar abduction). Moreover, the robot has no force sensors which are used in many approaches to determine confi guration. In this paper, we deal with the handover confi gurations that are pleasant and acceptable for humans and can be executed by a commercial Pepper robot. The confi gurations are based on an experiment where subjects teach a robot proper and improper handover confi gurations (Figure 1). An analysis of the experiment reveals the interrelationship between the confi gurations and parameters. These parameters, based on previous studies, are: the height of the subject 9, 10, the posture of the subject (standing or sitting) 11, 12 and the type of object 13, 14. We chose these parameters, because a Pepper robot is capable of sensing them. Other common parameters (e.g., grasp force or joint effort 15) cannot be sensed by the robots default sensors therefore they are not evaluated. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Macau, China, November 4-8, 2019 978-1-7281-4003-2/19/$31.00 2019 IEEE7582 II. RELATED WORK The handing over of objects with robots is a widely discussed topic. Only certain aspects are considered in most studies. The temporal sequence and the general process of a handover, as well as the aspects connected with it are summarized in different works. From the point of view of the giver, the handover consists of several steps: (1) grab the object, (2) approach the receiver, (3) reach the arm with the object and (4) transfer the object 16. The handover confi guration is determined in the transition between the reach and the transfer phase, which can be described as the coordinating phase 17. There are different approaches for the determination of handover confi gurations. The confi guration is seen as an optimization problem, with an algorithm generating the confi guration by minimizing different predefi ned cost func- tions 18, 19. These cost functions refer to three con- straints, namely safety, visibility and arm comfort. Human- human and human-robot studies are additional approaches analyzing handover confi gurations. Various parameters deter- mined in the studies are used to generate the confi gurations. A common conclusion of the previous approaches is that the orientations of the end effectors depend on object types and object affordances, especially the type of grasp 20 that is specifi ed by the physical object properties restricts the orientation. The preferred handover orientation of some objects differs depending on the situation and can also deviate from the natural handover orientation 14. Other objects should be presented in their default orientation and their affordance (e.g. handles) should be considered 13. Handover confi gurations include not only the orientation but also the position and method of the transfer. The han- dover methods differ according to the environment and the activity of the receiver 12. For example, users sitting in an offi ce prefer an indirect handover (e.g., where the robot places the object on a table), while people standing in a lobby prefer a direct handover 11. The posture, whether the receiver is standing or sitting, is also a determinant of the height of the handover. According to 21, the preferred height of the handover for sitting subjects is 78.9 cm on average, which is referred to as the height just below the chest area. For standing people, handover heights between 15 and 24 cm above their lower back are proposed 10. This fi nding indicates dependencies between the handover position for standing people and the physical properties of the subjects (e.g., height, leg length or weight). These properties can be used to create a physical model of the subject and generate positions that can be achieved with as little effort as possible 22. III. OBJECTIVES Developing human-like movement models requires the consideration of handover confi gurations, but few experi- ments have been carried out to discover confi gurations for commercial robots that are usually underactuated. Therefore, we used static and predetermined confi gurations in our previ- ous study 8. However, we lacked the interaction of different parameters and handover confi gurations needed to implement a dynamic approach in the context of commercial robots. Thus, we conducted a novel user-study by teaching a robot proper and improper poses with physical contact to the robot. This experiment was also statistically analyzed to determine differences from existing approaches with more complex robots and to develop a practical model for commercial robots based on common parameters. Summarizing, the major contributions of this study are as follows. 1) We propose a simple solution to interactively teach robots poses that can be used for different experiments. 2) We check whether the proper confi gurations are limited to a specifi c space or are distributed over the workspace of the commercial robot. The confi gurations are consid- ered both in the Cartesian space and in the confi guration space of the commercial robot and compared with the confi gurations from the existing literature to detect deviations. 3) We analyze the results statistically to determine cor- relations between the handover confi guration and the parameters and to develop a possible model. These results are compared with confi gurations from similar studies for more complex robots. The parameters are the receivers posture (sitting or standing), the receivers height and the type of object. These parameters are selected based on previous studies to allow for a com- parison of the correlations. IV. EXPERIMENT A. Experimental Setup We conducted a human-robot experiment to record han- dover confi gurations and the associated environmental pa- rameters. In this experiment, a Pepper robot was the giver and the human was the receiver. The Pepper robot was 121 cm high and had two arms with fi ve degrees of freedom each. The subject and the robot were initially positioned 66 cm apart (Figure 2). This distance corresponds to the mean value of the preferred distances from a study 21 of a robot approaching during handovers. The robot used a voice output to instruct the subject. Inputs from the subject were received via the tablet on the chest of the robot. 66 cm 66 cm Fig. 2.The two different receiver postures - standing and sitting. 7583 The fi ve objects shown in Figure 3 were placed on a table beside the robot. The objects were selected based on several criteria. For comparability objects were selected that were also used in other studies 14, 13. The selection was also limited by the restricted handling capabilities of the Pepper robot. Therefore no heavy or large objects could be chosen. In addition, we chose objects that have a dedicated orienta- tion in order to consider affordance axes 14. Furthermore, objects were selected based on the possible grasps of the grasp taxonomy 20. Because the robot had only a one- degree-of-freedom hand, several grasp types were omitted. Therefore, we limited the grasps to those where the thumb is abducted and those that are categorized as Power-Palm 2-5 and Precision-Pad 2-5. Based on these criteria, we selected the following objects: a toy, cup, box, bottle and ball. Fig. 3.Objects used in study. Left to right: (1) toy, (2) cup, (3) box, (4) bottle, (5) ball TABLE I PHYSICAL PROPERTIES OF THE TEST OBJECTS,INCLUDING THEIR MASSES AND DIMENSIONS BallBottleBoxCupToy Mass g125220201050 Width cm6.56.56.597 Height cm6.5233108 Depth cm6.56.52299 ShapeSphereCylinderCuboidConeDuck B. Procedure The experiment was carried out autonomously by the robot to increase the interactive feeling for the subject and to create equivalent conditions for each subject. First, the robot introduced the experiment and instructed the subject. The subject was instructed to move the right robot arm into different handover confi gurations. The subject had to touch the back of the robot hand to reduce the stiffness of the robot joints in the arm. After the hand was released, the stiffness was restored, and the robot held the confi guration. The subject could repeat this procedure until a desired confi guration was reached. Afterwards, the subject opened the hand of the robot by touching the robots head and placing the current handover object in the hand. Another head touch closed the hand. Finally, the subject could indicate using on Peppers tablet whether the selected confi guration was proper or improper. Then, the robot stored the data of the joint confi gurations and the pose of the end-effector and took an image of the object in the hand. After recording the pose, the robot returned to its initial position with both arms resting on the sides of its body. This procedure was repeated fi ve times for each object, with the subjects choosing an equal distribution between proper and improper poses. The robot informed the subject when the object had to be changed. After the experiment was performed for each object, a chair was provided, and the robot instructed the subject to sit on it. The subject had to specify fi ve handover confi gurations for each object, this time in a seated position. Personal data were collected at the end of the experiment, including the subjects height, age, gender, and the laterality. C. Hypotheses Although users have different preferences for handover confi gurations, we hypothesized that there would be a com- mon understanding of proper confi gurations. Based on this common understanding, we expected that these proper poses can be grouped and that these groups have observable patterns. If such groups and patterns can be detected, they may be random or infl uenced by other factors. We hypoth- esized that this grouping would be due to external factors. We expected that the object and grasp type infl uence the orientation of robots end-effector and that the height of the handover would depend on the height and pose of the subject. Furthermore, we assumed that there are differences in confi gurations for commercial and complex robots. Stated formally, our hypotheses are as follows: H1: If a general understanding of proper poses exists, then the proper poses chosen by the subjects are concentrated around the values of each variable. H2: There are observable patterns in the joint space for all proper handover confi gurations. H3: The object type and grasp type affect the handover confi guration. H4: The combination of receivers height and posture affects the height of a proper handover confi guration. D. Participant Population We recruited 20 participants consisting of researchers and students with various background to run the experiment. Of these participants, four were female and 16 were male. The subjects were between 20 and 50 years old (M = 28.4, SD = 6.9). The shortest participant was 160 cm and the tallest 193 cm tall (M = 180.0, SD = 8.0). As for previous experience with robots the evaluation scale ranged from 1: No previous experience with robots to 5: Programming experience with robots. The average value was 3.1 (SD = 1.0, Min = 2.0, Max = 5.0). On the question of handedness, two subjects stated that they were left-handed and 18 right- handed. No one claimed to be ambidextrous. E. Collected Data A total of 1000 handovers were recorded in the experiment - 500 with seated and 500 with standing subjects. Since the 7584 Rating ProperImproper Cartesian Pose X (m) ,400 ,300 ,200 ,100 ,000 -,100 -,200 -,300 Seite 1 Rating Cartesian Pose Y (m) ,000 -,100 -,200 -,300 -,400 -,500 -,600 Seite 1 Improper Proper Rating Cartesian Pose Z (m) 1,500 1,000 ,500 ,000 Seite 1 Improper Proper Fig. 4.Box plots of the handover positions in the Cartesian space on the three axes with case distinction for sitting and standing subjects. The improper and proper positions are grouped together. subjects could determine the distribution of the improper and proper confi gurations themselves, 450 improper and 550 proper confi gurations were recorded. The confi guration of the fi ve joints was saved for each handover. The Cartesian poses (location and orientation) of the end-effector could also be determined from this information, since the remaining robot joints, like the hip or torso, did not move. In addition, the robot took an image of each handover pose with its head camera to analyze the grasp and orientation of the object. 10.1% of all images (1.4% of the proper poses and 20.4% of the improper poses) had to be excluded from this analysis because the robot obscured the object with a body part or because of the limitations of the neck joints. In addition, the subject, the subject data, the positioning of the subject (standing or sitting) and the rating (proper or improper) were recorded for each handover. The col- lected data are available here: Minden/pepper-handover-pose-dataset. Position Y (m) ,00-,10-,20-,30-,40-,50-,60 Position Z (m) 1,40 1,20 1,00 ,80 ,60 ,40 Position X (m) ,40,20,00-,20 Seite 1 Fig. 5.Calculated end-effector positions in Cartesian space for standing and sitting subjects. The origin of the space is centrally located below the base of the robot. Axes were exchanged for visualization purposes. Proper poses are shown in green, the improper ones in red. V. RESULTS A. Common Understanding We analyzed the distribution of poses in the Cartesian space to evaluate if there was a common understanding of proper handover confi gurations for commercial robots. To this end, we considered the variance of the proper and improper poses. If the variance of these two groups of poses is homogeneous in this space, then the proper poses would not be clearly identifi able. Under the assumption that the improper poses are scattered around in this space. The variances of the poses in the x-dimension dif- fered signifi cantlyandheterogeneousvariancescould be assumed, as a Levene test illustrated (Levene test: F(1, 998) = 410.63, p 0.001, n = 1000). The descrip- tive statis