JP-7856691-B2 - Object grasping method, object grasping program, object grasping device, learning method, learning program, and learning device

Inventors

• 山▲崎▼ 岳
• 河合 圭悟
• 尾山 拓未
• 陶山 峻
• 中山 一隆
• 組谷 英俊
• 中川 浩
• 岡野原 大輔
• 奥田 遼介
• 松元 叡一

Assignees

• ファナック株式会社
• 株式会社Preferred Networks

Dates

Publication Date

20260511

Application Date

20240312

Priority Date

20150731

Claims (19)

1. The steps include: acquiring image data in which at least one processor includes at least either first image information of a plurality of objects or second image information of a plurality of objects obtained by processing the first image information; The steps include: the at least one processor inputting the image data of the plurality of objects into a neural network to obtain information for grasping one of the plurality of objects; The process includes the step of controlling a gripping unit to grip an object using information for gripping the object, The aforementioned neural network was trained based on data obtained at least from object grasping simulations. The information for grasping the object includes at least one of the following: robot control information, position information of the grasping part, posture information of the grasping part, information regarding the retrieval direction of the grasping part, position information of the object, information regarding the probability of successful grasping of the object, or control information of a measuring instrument. How to grasp objects.
2. The aforementioned neural network was trained using rewards calculated based on object grasping information. The method for gripping an object according to claim 1.
3. The information on gripping the object includes at least one of the following: whether or not the object was gripped, the number of times the object was successfully gripped, the time required to grip and transport the object, the force applied to the gripping part, the degree of achievement in the subsequent process after gripping the object, the state of the object, or the energy required to grip and transport the object. The method for gripping an object according to claim 2.
4. The aforementioned neural network is a value function in reinforcement learning, The method for gripping an object according to any one of claims 1 to 3.
5. Based on the type of gripping part used to grip the object, the value function is selected from a plurality of value functions. The method for gripping an object according to claim 4.
6. The aforementioned neural network is trained to minimize the error calculated based on the data obtained from the simulation and the output of the neural network. The method for gripping an object according to claim 1.
7. The neural network outputs at least one of the following: location information of the object or information regarding the probability of successfully grasping the object. The method for gripping an object according to claim 6.
8. The system further comprises the step of determining whether the information for grasping the object is abnormal or not. The method for gripping an object according to any one of claims 1 to 7.
9. The first image information includes distance information from the measuring instrument to the surfaces of the plurality of objects. The method for gripping an object according to any one of claims 1 to 8.
10. The second image information includes distance information from the measuring instrument to the surfaces of the plurality of objects. The method for gripping an object according to any one of claims 1 to 9.
11. The measuring instrument is mounted on the arm of a robot that grasps the object. The method for gripping an object according to claim 9 or 10.
12. An object gripping program for causing at least one computer to execute the object gripping method described in any one of claims 1 to 11.
13. At least one memory, It comprises at least one processor, The at least one processor performs the object gripping method according to any one of claims 1 to 11. Object grasping device.
14. The steps include: acquiring image data in which at least one processor includes at least either first image information of a plurality of objects or second image information of a plurality of objects obtained by processing the first image information; The steps include: the at least one processor inputting the image data of the plurality of objects into a neural network, causing the neural network to output information for grasping one of the plurality of objects; The steps include: the at least one processor calculating an error based on the output of the neural network and the label; The steps include: the at least one processor updating the neural network according to the error; Equipped with, The aforementioned labels are data obtained from an object grasping simulation. The information for grasping the object includes at least one of the following: location information of the object, information regarding the probability of successfully grasping the object, information indicating whether or not the object was grasped, or information indicating the result of grasping the object. Learning methods.
15. The first image information includes distance information from the measuring instrument to the surfaces of the plurality of objects. The learning method according to claim 14.
16. The second image information includes distance information from the measuring instrument to the surfaces of the plurality of objects. The learning method according to claim 14 or 15.
17. The measuring instrument is mounted on the arm of a robot that grasps the object. The learning method according to claim 15 or 16.
18. A learning program for causing at least one computer to execute the learning method described in any one of claims 14 to 17.
19. At least one memory, It comprises at least one processor, The at least one processor performs the learning method according to any one of claims 14 to 18. Learning device.

Description

This invention relates to a machine learning device, a robotic system, and a machine learning method for learning how to retrieve workpieces that are randomly placed, including in a stacked state. For some time, robot systems have been known that, for example, grasp and transport workpieces loosely stacked in a basket-like box using the robot's hand (see, for example, Patent Documents 1 and 2). In such robot systems, for example, a three-dimensional measuring instrument installed above the basket-like box is used to acquire positional information of multiple workpieces, and the workpieces are picked up one by one by the robot's hand based on that positional information. Patent No. 5642738Patent No. 5670397 Figure 1 is a block diagram showing a conceptual configuration of a robotic system according to one embodiment of the present invention.Figure 2 is a schematic diagram illustrating a model of a neuron.Figure 3 schematically shows a three-layer neural network constructed by combining the neurons shown in Figure 2.Figure 4 is a flowchart showing an example of the operation of the machine learning device shown in Figure 1.Figure 5 is a block diagram showing a conceptual configuration of a robot system according to another embodiment of the present invention.Figure 6 is a diagram illustrating an example of the preprocessing steps in the robot system shown in Figure 5.Figure 7 is a block diagram showing a modified version of the robot system shown in Figure 1. The following describes in detail embodiments of the machine learning apparatus, robot system, and machine learning method according to the present invention, with reference to the attached drawings. In each drawing, the same component is denoted by the same reference numeral. Furthermore, components denoted by the same reference numeral in different drawings are considered to have the same function. For ease of understanding, the scale of these drawings has been appropriately adjusted. Figure 1 is a block diagram showing the conceptual configuration of a robot system according to one embodiment of the present invention. The robot system 10 of this embodiment comprises a robot 14 with a hand portion 13 attached for gripping workpieces 12 loosely stacked in a cage-like box 11; a three-dimensional measuring instrument 15 for measuring a three-dimensional map of the surface of the workpieces 12; a control device 16 for controlling the robot 14 and the three-dimensional measuring instrument 15, respectively; a coordinate calculation unit 19; and a machine learning device 20. Here, the machine learning device 20 comprises a state quantity observation unit 21, an operation result acquisition unit 26, a learning unit 22, and a decision-making unit 25. As will be described in detail later, the machine learning device 20 learns and outputs manipulated quantities such as command data for instructing the robot 14 to retrieve the workpiece 12, or measurement parameters of the three-dimensional measuring instrument 15. The robot 14 is, for example, a six-axis articulated robot, and the drive axes of both the robot 14 and the hand unit 13 are controlled by the control device 16. The robot 14 is used to pick up workpieces 12 one by one from a box 11 placed in a predetermined location and move them sequentially to a designated location, such as a conveyor or workbench (not shown). Incidentally, when removing the loosely stacked workpieces 12 from the box 11, the hand unit 13 or the workpiece 12 may collide with or come into contact with the wall of the box 11. Alternatively, the hand unit 13 or the workpiece 12 may get caught on another workpiece 12. In such cases, a function to detect the force acting on the hand unit 13 is necessary to immediately avoid overloading the robot 14. Therefore, a six-axis force sensor 17 is provided between the tip of the robot arm 14 and the hand unit 13. Furthermore, the robot system 10 of this embodiment also includes a function to estimate the force acting on the hand unit 13 based on the current values of the motors (not shown) that drive the drive axes of each joint of the robot 14. Furthermore, since the force sensor 17 can detect the force acting on the hand unit 13, it can also determine whether the hand unit 13 is actually gripping the workpiece 12. In other words, when the hand unit 13 grips the workpiece 12, the weight of the workpiece 12 acts on the hand unit 13. Therefore, after performing the workpiece removal operation, if the detected value of the force sensor 17 exceeds a predetermined threshold, it can be determined that the hand unit 13 is gripping the workpiece 12. Note that the determination of whether the hand unit 13 is gripping the workpiece 12 can also be made, for example, based on the image data from the camera used in the three-dimensional measuring instrument 15, or the output of a photoelectric sensor (not shown) attached to the hand unit 13. Alternatively, the determination may be made based on the pressure