A 3D Vision based Object Grasping Posture Learning System for Home Service Robots

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1 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Banff Center, Banff, Canada, October 5-8, 2017 A 3D Vision based Object Grasping Posture Learning System for Home Service Robots Yi-Lun Huang, Sheng-Pi Huang, Hsiang-Ting Chen, Yi-Hsuan Chen, Chin-Yin Liu, and Tzuu-Hseng S. Li airobots Lab., Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, ROC yuheng741@gmail.com, ken790724@hotmail.com, syou@liv .tw, g689526@hotmail.com, n @mail.ncku.edu.tw, and thsli@mail.ncku.edu.tw Abstract This paper proposes a 3D vision based object grasping posture learning system. In this system, the robot recognizes the orientation of the object to decide the grasping posture, whereas selects a feasible grasping point by detecting the surrounding. When the planned posture is not good enough, the proposed learning system adjusts the position of the end effector real time. The learning system is inspired by a book entitled, Thinking, Fast and Slow, and consists of two subsystems. The subsystem I judges whether the pose of the object is learned before, and plans a grasping posture by past experience. When the pose of the object is not learned before, the subsystem II learns a position adjustment by the real time information form the motor angels and the images. Finally, the method proposed in this paper is applied to the home service robot and is proven the feasibility by the experimental results. Keywords 3D vision cognition learning; grasping posture I. INTRODUCTION Grasping is one of the fundamental functions for home service robots, and it relatives to the vision recognition and motion planning abilities of the robot. For grasping the appointed object, the robot has to recognize it correct, and to plan a feasible grasping posture. In fact, this is not an easy task. Therefore, in this paper, we propose a posture selection method to choose a feasible posture, and a learning system to adjust the planned posture to improve the successful rate. There are many methods about image recognition and many of them extract features form images to construct an object model which is then used for matching new image. Among these methods, Scale-Invariant Feature Transform (SIFT) [1], Speeded-Up Robust Features (SURF) [2], Harris corner detector [3], and Features from Accelerated Segment Test (FAST) [4] are the most popular algorithms in recent years. In addition, more and more research use 3D models to represent and recognize objects [5] [6]. 3D models can provide more information and help the robot plan the grasping trajectory. Besides, some works focus on the object tracking problem [7] [8], and some others focus on classification from objects and background [9]-[11]. In this paper, we adapt SURF combining with the BRISK algorithm [13] for object recognition due to its stable performance. However, when the robot is grasping an object, the limitation of the motors, camera, and mechanism manufacture tolerances, the end-effector may not move to the planned position. Therefore, we propose a posture learning system for real time adjusting and learning the posture. The structure of the system is inspired by the book titled Thinking, Fast and Slow [14]. The author of the book, Daniel Kahneman, describes the ways of human thinking and making decisions by two systems: system 1 and system 2. System 1 uses intuitive thinking manner and makes a judgment very fast, whereas system 2 uses a logistic thinking manner and makes a decision though repeated verification. This proposed learning system is different from the existed learning algorithms, such as Q-learning [15], Neural Network [16], and Particle Swarm Optimization (PSO) [17] because of its structure. Integrating with two subsystem increase the resilience of the system. An example of throwing the ball into the basket [18] is referred. The rest of this paper is organized as follows. Section II describes the system overview of the proposed system. In section III, the method of selecting the grasping posture is presented, and the posture learning system is then described in section IV. Section V demonstrates the experimental results to show the effectiveness of the proposed system. Finally, the paper concludes in Section VI. II. SYSTEM OVERVIEW The flowchart of the proposed system is shown in Fig 1. The robot first recognizes the object by its recognition system, the position and the orientation of the object are then calculated. After that, we frame the region of interest and eliminate the information of the other region. The orientation of the object are simply divided into two orientations: Start Recognize the object Calculate the position and the orientation of the object Frame the region of interest and eliminate the others Select feasible grasping point candidates Generate a grasping posture by the learning system Grasp the object Fig. 1 The flowchart of the object grasping control strategy /17/$ IEEE 2690

2 vertical orientation and horizontal orientation. For each orientation, there are several grasping point candidates, and the robot selects a feasible candidate by detecting the surrounding of the object. For further improving the accuracy of grasping, we proposed a learning system to learn a compensation value of a grasping posture. There are two strategies in this learning system which is inspired by the book titled Thinking, Fast and Slow. The strategy I directly uses the past compensation value to generate a grasping posture, whereas the strategy II carefully learns a new compensation value. Finally, the robot uses the generated grasping posture to grasp the object. III. GRASPING POINT SELECTION When the robot wants to grasp an object, it has to recognize the object first and calculate the position and orientation of the object. The robot then plans the grasping posture by generating a suitable rotation and position of the end effector. This section describes how to select a region of interest first, and follows by how to select a suitable grasping point. A. Object Region Selection A RGB-D camera is mounted on the head of the robot to obtain visual information. The original 2D image is transferred into a 3D point cloud first, as shown in Fig. 2. Each point is represented as R,G,B,,,, where R, G, B are the value of red, green, and blue color, respectively, and,, are the position in the head coordinate. When the 2D image is transformed to a 3D point cloud, the amount of the information increases hugely. Therefore, we have to frame a region we are interesting in, and keep only the information in this region to simplify the point cloud. Since the object has been identified by the recognition system, we first set four points of the object as its four vertices to frame the region of the object, as shown in Fig. 3, and then extend a predefined space around the object to detect the obstacles. The extended region is then defined as the region of interest (ROI) for future usage. However, not all points in the ROI will be remained, only the points have similar depth with the object will be kept, the other points will be eliminated. The result of simplified point cloud is shown in Fig. 3. B. Grasping Posture Selection For an object on a table, it may be placed in vertical orientation or horizontal orientation, as shown in Fig. 4. We first identify the orientation of the object by calculating the distribution of the point clouds. We project all points belong to the object to the X -Z plane, as shown in Fig. 5, and calculate the variation of the points on the coordinate by the following equations De (1) where is the projected point on coordinate, N is the amount of the points, and De is the standard deviation of all points. Because the variation of a vertical object is much Fig. 4 Two orientations of the object. Vertical orientation. Horizontal orientation. Fig. 2 The result of 3D point cloud transformation. The 2D vision. the transferred 3D point cloud. Fig. 3 The results of simplifying the point cloud. The point cloud of the object region. The final simplified point cloud. Fig. 5 The projected points on X -Z plane. A vertical object. A horizontal object. 2691

3 larger than a horizontal object, we defined a binary variable that represents the orientation of the object with the formula:, S (2), where S denotes the orientation of the object, and represent the vertical orientation and horizontal orientation, respectively. When the variation is bigger than a predefined threshold,, the orientation of the object is judged as a vertical one, otherwise the object is horizontal. The orientation of the object decides the rotation of the endeffector, and the grasping point decides the grasping position. For each orientation, we define several possible grasping points, as shown in Fig 6. One of the possible grasping points is the center of the object, and another two grasping points move away from the center a quarter of object height along with the centerline. Therefore, a horizontal object has three possible grasping points. For a vertical object, there are another two possible grasping points on the top of the object. The number of grasping points represent their priority. The center of the object has the highest priority, the point above the center has the second priority, and the points on the top of the object have the lowest priority due to the difficulty of grasping. Each possible grasping points is a grasping point candidate. Every candidate will be checked whether it is a feasible one. If there is an obstacle on the path of the grasping trajectory, it will be record as an unfeasible one. Otherwise, it is a feasible grasping point. When there are more than one feasible grasping point, the grasping point with a smaller number will be chosen. Therefore, the robot can decide a suitable grasping position along with the rotation of the end effector. However, even if the robot plans a good posture, poor execution may cause a failure of grasping. The poor execution may due to the motor limitation, coordinate transformation, visual noise, and so on. For dealing with this problem, we propose a posture learning system which allows the robot to learn a workable posture by itself. IV. COGNITION LEARNING SYSTEM In this section, we will present the posture learning system which is inspired by a psychological book titled Thinking, Fast and Slow written by the Nobel prize winner Daniel Kahneman. He leverages two systems, system I and system II, to model the structure of human thing and describe how people use these two systems to think and to make decisions. System I uses an intuitive thinking manner and makes a decision mainly based on the past experience. So, it can deal with a huge amount of information and make a judgment in a very short period. For example, when we see a dog, we know it is a dog almost without thinking. In other words, it is an unconscious process. People make most of their decisions in the daily life and they do not know they have do so. Benefit from the system I, we can process huge information from the complex environment using the minimal effort. On the contrary, system II uses a logistic thinking manner and makes a decision though repeated verification. It takes a lot of time and effort. Sometimes, it is a learning process, and of course, it is conscious. Human use these two systems alternately to minimize the effort they have to make while maximize the performance by concentrating on few events. In spired by the two systems structure of human thinking, we propose a gesture learning system in which there are also two subsystems. The overall flowchart is shown in Fig. 7. After the robot recognizing the object, subsystem I will judge whether the object pose has been grasped before, if the answer is positive, the past experience will be retrieved to use directly. If not, subsystem I will generate a grasping posture by the method mentioned in section III. However, the generated grasping posture may not precise enough due to the limitations of motor, camera, and other reasons. Therefore, the robot Start System 1 Grasping posture errors calculation e, e m v or times k No System 2 Grasping experience database Yes Update the database Grasp the object Fig. 6 The grasping point candidates The candidates of a horizontal object. The candidates of a vertical object. Fig. 7 The overall flowchart of grasping posture learning system. 2692

4 detects the errors and adapts subsystem II to learn a posture compensation to improve the grasping accuracy. The adjusted grasping posture will be verified repeatedly until the object can be grasped successfully. And then, the successful posture will be recorded in the grasping experience database. A. System I: generating a grasping posture Fig 8 illustrates the flowchart of the subsystem I in which there are two ways to generate a grasping posture. Based on we mentioned in section III, when the robot wants to grasp an object, it has to judge the orientation of the object and detect the obstacles around the object to plan a suitable grasping point and rotation of its end effector. Even though the process of posture generation is not complex, it still takes computation time and load. Inspired by the human intuitive thinking manner, we set a grasping posture database which stores all past grasping experiences. When the pose of the object is grasped before, the robot will use the experience directly without regenerating a posture. Therefore, the computation time and load are saved. B. System II: adhusting the grasping posture Sometimes, the robot end effect cannot move to the planed position in the execution, for example, an overweight load will lead to a drop of the robotic arm, as shown in Fig. 9. When the grasping posture generated by the subsystem I is fail, the subsystem II will learn a better grasping posture by calculating a compensation position for the command of the end-effector. The flowchart of the subsystem II is shown in Fig.10. The errors between the real position and the ideal position are calculated from the image and the feedback of the motors, separately. Then the errors are used to adjust the position of the end effector until they are smaller than a threshold. Start Is the pose of the object grasped before? No Calculate the object pose Yes Grasping experience database Retrieve the grasping posture from the database Covert the object pose to the grasping posture Fig. 8 The flowchart of System 1. Start Calculate the real position of the end-effector by forward kinematics and by image Calculate the distance errors between the real position and the ideal position e, e Adjust a new posture by error compensation and move the end-effector Xˆ r Are the errors smaller than the threshold? Yes Record the adjusted position m Fig. 10 The flowchart of System 2. v 1) The posture error caculated by motor feedback Fig. 12 indicates the position error of the end-effector between the ideal position and real position. The real position is represented as,, and the real position is,,. The ideal position is planned by the posture generation method, while the real position is calculated by the forward kinematics and the feedback of every motor angle. So, the position error,, can be calculated by the following equation: (3) where,, and. 2) The posture error caculated by image Besides, we use the red plastic sleeve on the robot s endeffector to recognize its position, as shown in Fig. 12. The image coordinate of the finger is presented as 0 (4) where λ is a constant value, and are the position on the image in x-axis and y-axis, respectively. α and β are the scale factors, and is a skew factor. (, ) is a principal point, R is a rotation matrix, and T is a translation matrix. Therefore, No Fig. 9 The execution errors of the robot end effector. Fig. 11 The picture that indicates the error of the robotic arm posture. 2693

5 the distance error,, between the ideal position and the real position is computed by e x y z (5) where,,. Now we have two position errors, and, calculated form the motor feedback and the image obtained by the robot s camera, respectively. Both information are used to adapt the position command for the end-effector by the following equation (6) where is the adjusted grasping position and it is adjusted by the position errors. and are regular weights with the range of [0,1]. Therefore the robot can adjust its posture slowly by the information obtained by its own sensors. When both errors are smaller than a predefined threshold, ε, the adjusted posture will be recorded and save in the grasping experience database for future usage. V. EXPERIMENTS In this section, we demonstrate two experiments to show the effectiveness of our posture selection method and the posture learning system, respectively. A. Experiments for the Posture Selection In the first experiment, we show the results of posture selection in a variety of situations. Fig. 13 shows the interface of posture selection in which all grasping point candidates are listed in the right side of the picture. The top five candidates belong to the vertical orientation, while the bottom three candidates belong to the horizontal orientation. The feasibility of each point is represented by color where green color represents feasible and pink color is not. The left side of the interface is the calculated 3D point cloud of ROI. Fig. 14 shows the posture selection results among different situations. An obstacle is randomly placed around the target object to block some grasping solutions. We tested three situations for each orientation. In the first one, there was no obstacle around the object, so all candidates should be feasible, and the experiment result showed the system judged correct, as shown in Fig. 14 and Fig. 14(d). In the second and tired situation, there are one or more obstacles around the object, one can see that our proposed method successfully select the feasible solutions according to the real situation to avoiding Situation 1: vertical object with no obstacle around it. Situation 2: vertical object with one obstacle on its left side. (c) Situation 3: vertical object with one obstacle on its top. (d) Situation 4: horizontal object with no obstacle around it. Fig. 12 The real position of end-effector with visual system. (e) Situation 5: horizontal object with one obstacle around it. Fig. 13 The posture selection interface. (f) Situation 6: horizontal object with one obstacle around it. Fig. 14. The posture selection results among six situations. 2694

6 collision, as shown in Fig. 14, Fig. 14(c), Fig. 14(e), and Fig. 14(f). B. Experiments for Cognition Learning System In the second experiment, the target object was placed on the edge of the table with different ranges and the robot had to grasp it. The robot learned the grasping posture by the learning system. Fig. 15 demonstrates the learning process. At first, the object was so close to the robot fingers, as shown in Fig.15. The learning system then used the information from the motor angles and the images to slowly adjust the position of the endeffector, as shown in Fig.15 and Fig.15 (c). At the end, the object was close to the middle position of the gripper. After learning the robot can successfully grasp the object, as shown in Fig.15 (d). VI. CONCLUSION This paper has proposed a 3D vision based object grasping posture learning system for home service robot which allows the robot real time adjust the position of its end-effector by itself. The grasping posture selection method judged the orientation of the object and the obstacles around the object to choose a feasible grasping posture. When the planned posture is not good enough, the learning system calculated the real position of the end-effector and compared it with the ideal position to adjust the posture. The adjusted posture was recorded in the grasping posture database. When the object was placed in the position the robot learned, the learned posture will be retrieved. The experiment results have shown that the robot selected the feasible postures successfully, and improved the selected grasping posture by the learning system. ACKNOWLEDGMENT This work supported by the Ministry of Science and Technology, Taiwan (ROC), under grant MOST E MY2 is gratefully acknowledged. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] (c) [13] [14] [15] [16] [17] Fig. 15 (d) Snapshots of the grasping posture learning process. [18] 2695 D. G. Lowe, Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, vol. 60, no. 2, pp , H. Bay, T. Tuytelaars, and L. V. Gool, SURF: Speeded-up robust features, in Proceedings of the European Conference on Computer Vision, pp , C. G. Harris and M. J. Stephens, A combined corner and edge detector, in Proceedings of 4th Alvey Vision Conference, pp , E. Rosten and T. Drummond, Faster and better: A machine learning approach to corner detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 1, pp , M. Z. Zia, M. Stark, and K. Schindler, Explicit occlusion modeling for 3D object class representations, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp , M. Z. Zia, M. Stark, B. Schiele, and K. Shindler, Detailed 3D representations for object recognition and modeling, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 11, pp , M. Xu, T. Ellis, S. Godsill, and G. Jones, Visual tracking of partially observable targets with suboptimal filtering, IET Computer Vision, vol. 5, pp. 1-13, M. Tian, W. Zhang, and F. Liu, On-line ensemble SVM for robust object tracking, Computer Vision ACCV 2007, pp , H. Grabner, M. Grabner, and H. Bischof, Real-time tracking via online boosting, in Proceedings of British Machine Vision Conference, vol. 1, pp , H. Grabner, C. Leistner, and H. Bischof, Semi-supervised online boosting for robust tracking, in Proceedings of European Conference on Computer Vision, Q. Wang, F. Chen, J. Yang, W. Xu, and M. Yang, Transferring visual prior for online object tracking, IEEE Transactions on Image Processing, vol. 21, pp , Z. Kalal, J. Matas, and K. Mikolajczyk, "Tracking-learning-detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 7, pp , S. Leutenegger, M. Chli, and R. Siegwart, BRISK: Binary robust invariant scalable keypoints, in Proceedings of the IEEE International Conference on Computer Vision, pp , D. Kahneman, Thinking, Fast and Slow. Penguin Group UK, R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. Cambrigde, MA: MIT press, 1998 Wikipedia for neural network, Available: J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, pp , T.-H. S. Li, et al, Robots That Think Fast and Slow: An Example of Throwing the Ball into the Basket, IEEE Access, pp , 2016.

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