Abstract: A waste sorting robot gripper comprises a suction cup engageable with the surface of a waste object. The suction cup has an air hole for evacuating air from the suction cup. A suction tube is coupled to the suction cup. The suction tube comprises a longitudinal axis. A first air inlet is in fluid communication with the air hole at one end of the suction tube and an air outlet at the other end of the suction tube. A path of the air flow between the air inlet and the air outlet is substantially along the longitudinal axis. The suction tube comprises a second air inlet in fluid communication with an air source, the second air inlet being between the first air inlet and the air outlet.

Abstract: Feedback information is an important aspect in all machine learning systems. In robot systems that are picking objects from a plurality of objects this has been arranged by acquiring images of objects that have been picked. When images are acquired after picking they can be imaged accurately and the information about picking and also dropping success can be improved by using acquired images as a feedback in the machine learning arrangement being used for controlling the picking and dropping of objects.

Abstract: A waste sorting robot can include a manipulator comprising a suction gripper for interacting with one or more waste objects to be sorted within a working area, and wherein the manipulator is moveable within the working area. There is a controller configured to send control instructions to the manipulator. At least one pressure sensor is in fluid communication with the suction gripper and configured to generate a pressure signal in dependence on a fluid pressure in the suction gripper. The controller is configured to receive the pressure signal and to determine manipulator instructions in dependence on the pressure signal.

Abstract: Feedback information is an important aspect in all machine learning systems. In robot systems that are picking objects from a plurality of objects this has been arranged by acquiring images of objects that have been picked. When images are acquired after picking they can be imaged accurately and the information about picking and also dropping success can be improved by using acquired images as a feedback in the machine learning arrangement being used for controlling the picking and dropping of objects.

Abstract: Feedback information is an important aspect in all machine learning systems. In robot systems that are picking objects from a plurality of objects this has been arranged by acquiring images of objects that have been picked. When images are acquired after picking they can be imaged accurately and the information about picking and also dropping success can be improved by using acquired images as a feedback in the machine learning arrangement being used for controlling the picking and dropping of objects.

Abstract: The invention concerns in general the technical field of robotics and automation. Especially the invention concerns a structure for improving a shock tolerance of a robot or other positioning system. More specifically, the invention discloses a mounting element structure for increasing shock tolerance in a robot. The mounting element structure includes a first surface (201) and a second surface (202) towards a robot tool element, wherein the first and second surfaces (201; 202) are configured to be connected with a string assembly (203). The string assembly is configured to, under exposure of external force exceeding a predetermined level, to reduce the damage caused by the force by deforming the shape of the string assembly (203).

Abstract: According to one aspect of the invention, there is provided a method comprising: obtaining at least one image comprising at least one object; analyzing the at least one image to determine at least one gripping location to grip an object; selecting a gripping location from the at least one gripping location based on a predetermined criterion; and issuing at least one instruction to a gripper to grip the object at the selected gripping location.

Abstract: A method in which an apparatus receives first sensor data from first sensors and determines a target position from the data, the target position may be a position in space or an orientation of a gripper in a robot arm First instructions are issued to the robot arm or the gripper in order to move a gripper to the target position. Force feedback sensor data is received from force feedback sensors associated with either the robot arm or the gripper or from the first sensors. A failure in carrying out the first instructions is determined. Second sensor data is received from the at least one first sensor. Successful gripping of an object is determined from the second sensor data.

Abstract: The invention relates to a method and system for invalidating sensor measurements after a sorting action on a target area of a robot sorting system. In the method there are obtained sensor measurements using sensors from a target area. A first image is captured of the target area using a sensor over the target area. A first sorting action is performed in the target area using a robot arm based on the sensor measurements and the first image. Thereupon, a second image of the target area is captured using a sensor over the target area. The first and the second images are compared to determine invalid areas in the target area. The invalid areas are avoided in future sorting actions based on the sensor measurements.

Abstract: According to one aspect of the invention, there is provided a method comprising: obtaining at least one image comprising at least one object; analysing the at least one image to determine at least one gripping location to grip an object; selecting a gripping location from the at least one gripping location based on a predetermined criterion; and issuing at least one instruction to a gripper to grip the object at the selected gripping location.

Abstract: The invention relates to a method and system for recognizing physical objects. In the method an object is gripped with a gripper, which is attached to a robot arm or mounted separately. Using an image sensor, a plurality of source images of an area comprising the object is captured while the object is moved with the robot arm. The camera is configured to move along the gripper, attached to the gripper or otherwise able to monitor the movement of the gripper. Moving image elements are extracted from the plurality of source images by computing a variance image from the source images and forming a filtering image from the variance image. A result image is obtained by using the filtering image as a bitmask. The result image is used for classifying the gripped object.

Abstract: A method in which an apparatus receives first sensor data from first sensors and determines a target position from the data, the target position may be a position in space or an orientation of a gripper in a robot arm First instructions are issued to the robot arm or the gripper in order to move a gripper to the target position. Force feedback sensor data is received from force feedback sensors associated with either the robot arm or the gripper or from the first sensors. A failure in carrying out the first instructions is determined Second sensor data is received from the at least one first sensor. Successful gripping of an object is determined from the second sensor data.

Abstract: In the method of the invention, information is selected in a processing unit. The end result to be achieved is final feature information to be used for performing a task. In the method, input information to the processing unit is treated with the intention to find useful information from the input information in view of the task. Feature information representing characteristics of the input information is formed from the input data, the values of the feature information being further processed on the basis of their utility. The method is mainly characterized in that a preliminary prediction is formed from a first context of a sot of input information by means of first parameters and in that the first primary input of the set of input data is preprocessed by forming calculation results by means of second parameters.

Abstract: In the method of the invention, information is selected in a processing unit. The end result to be achieved is final feature information to be used for performing a task. In the method, input information to the processing unit is treated with the intention to find useful information from the input information in view of the task. The input information consists of a primary input and a context representing general information related to the task to be performed. Feature information representing characteristics of the input information is formed from the input data, the values of the feature information being further processed on the basis of their utility. The method is mainly characterized in that a preliminary prediction is formed from a first context of a set of input information by means of first parameters and in that the first primary input of the set of input data is preprocessed by forming calculation results by means of second parameters.