A depth image is used to obtain a three dimensional (3D) geometry of an object as an object mesh. The object mesh is obtained using an object shell representation. The object shell representation is based on a series of depth images denoting the entry and exit points on the object surface that camera rays would pass through. Given a set of entry points in the form of a masked depth image of an object, an object shell (an entry image and an exit image) is generated. Since entry and exit images contain neighborhood information given by pixel adjacency, the entry and exit images provide partial meshes of the object which are stitched together in linear time using the contours of the entry and exit images. A complete object mesh is provided in the camera coordinate frame.

An object induction system is disclosed for assigning handling parameters to an object. The system includes an analysis system, an association system, and an assignment system. The analysis system includes at least one characteristic perception system for providing perception data regarding an object to be processed. The association system includes an object information database and assigns association data to the object responsive to commonality with of any of the characteristic perception data with any of the characteristic recorded data. The assignment system is for assigning programmable motion device handling parameters to the indicia perception data based on the association data, and includes a workflow management system as well as a separate operational controller.

A method includes receiving sensor data representing a first object in an environment and generating, based on the sensor data, a first state vector that represents physical properties of the first object. The method also includes generating, by a first machine learning model and based on the first state vector and a second state vector that represents physical properties of a second object previously observed in the environment, a metric indicating a likelihood that the first object is the same as the second object. The method further includes determining, based on the metric, to update the second state vector and updating, by a second machine learning model configured to maintain the second state vector over time and based on the first state vector, the second state vector to incorporate into the second state vector information concerning physical properties of the second object as represented in the first state vector.

Whether or not workpieces are present in a workpiece storage area is determined based on an image acquired by image capturing. When the workpieces are determined to be present, whether or not a crossing part is present in the workpiece storage area is determined based on the image, the crossing part being a part where soft body portions of a plurality of workpieces cross each other in an overlapping manner. When the crossing part is determined to be present, an uppermost soft body portion placed at an uppermost position among the soft body portions crossing each other is determined based on the image. A workpiece including the uppermost soft body portion thus determined is determined as an uppermost workpiece placed at an uppermost position.

Various embodiments include a method for a robot to grab a 3D object. The method may include: determining a current position and attitude of a visual sensor of the robot relative to the 3D object; acquiring a grabbing template of the 3D object, the grabbing template comprising a specified grabbing position and attitude of the visual sensor relative to the 3D object; judging whether the grabbing template further comprises at least one reference grabbing position and attitude of the visual sensor relative to the 3D object, wherein the reference grabbing position and attitude is generated on the basis of the specified grabbing position and attitude; and based on a judgment result, using the grabbing template and the current position and attitude to generate a grabbing position and attitude of the robot.

A method for performing a task by a robotic device includes mapping a group of task image pixel descriptors associated with a first group of pixels in a task image of a task environment to a group of teaching image pixel descriptors associated with a second group of pixels in a teaching image based on positioning the robotic device within the task environment. The method also includes determining a relative transform between the task image and the teaching image based on mapping the plurality of task image pixel descriptors. The relative transform indicates a change in one or more of points of 3D space between the task image and the teaching image. The method also includes performing the task associated with the set of parameterized behaviors based on updating one or more parameters of a set of parameterized behaviors associated with the teaching image based on determining the relative transform.

A method includes receiving sensor data representing a first object in an environment and generating, based on the sensor data, a first state vector that represents physical properties of the first object. The method also includes generating, by a first machine learning model and based on the first state vector and a second state vector that represents physical properties of a second object previously observed in the environment, a metric indicating a likelihood that the first object is the same as the second object. The method further includes determining, based on the metric, to update the second state vector and updating, by a second machine learning model configured to maintain the second state vector over time and based on the first state vector, the second state vector to incorporate into the second state vector information concerning physical properties of the second object as represented in the first state vector.

Provided are a method and robot device for sharing object data. The method, performed by a robot device, of sharing object data includes: obtaining sensing data related to objects in a certain space; classifying the obtained sensing data into a plurality of pieces of object data based on properties of the objects; selecting another robot device from among at least one other robot device; selecting object data to be provided to the selected robot device from among the classified plurality of pieces of object data; and transmitting the selected object data to the selected robot device, wherein the classifying of the sensing data into a plurality of pieces object data includes generating a plurality of data layers including the classified plurality of pieces of object data.

Example embodiments may relate to methods and systems for selecting a grasp point on an object. In particular, a robotic manipulator may identify characteristics of a physical object within a physical environment. Based on the identified characteristics, the robotic manipulator may determine potential grasp points on the physical object corresponding to points at which a gripper attached to the robotic manipulator is operable to grip the physical object. Subsequently, the robotic manipulator may determine a motion path for the gripper to follow in order to move the physical object to a drop-off location for the physical object and then select a grasp point, from the potential grasp points, based on the determined motion path. After selecting the grasp point, the robotic manipulator may grip the physical object at the selected grasp point with the gripper and move the physical object through the determined motion path to the drop-off location.

A method for assembling a cooling apparatus having a cabinet which houses an inner casing defining at least one compartment for the storage of products to be cooled and one or more objects configured to be connected to the inner casing. The method includes: providing the inner casing; automatically univocally identifying the model of the inner casing among various known inner casing models by using a detecting device and performing a step of connecting the one or more objects to the inner casing based on the model of inner casing identified in the identifying step.