A method and computing system for performing object detection are presented. The computing system may be configured to: receive first image information that represents at least a first portion of an object structure of an object in a camera's field of view, wherein the first image information is associate with a first camera pose; generate or update, based on the first image information, sensed structure information representing the object structure; identify an object corner associated with the object structure; cause the robot arm to move the camera to a second camera pose in which the camera is pointed at the object corner; receive second image information associated with the second camera pose; update the sensed structure information based on the second image information; determine, based on the updated sensed structure information, an object type associated with the object; determine one or more robot interaction locations based on the object type.

An apparatus including a combination possibility calculation unit to calculate a stable orientation in which, from three-dimensional shape data of a part, the part is stabilized on a flat surface, to calculate a grasping method for grasping the part with a hand, and to calculate a combination in which the hand does not interfere from system configuration data including information on a connection destination of the hand and a combination group of the grasping method and the stable orientation; a regrasping path calculation unit to calculate a regrasping path of the part by using the calculated combination; a path group calculation unit to calculate a path having the minimum number of teaching points from the regrasping path as a path group based on orientation data for designating an input orientation and an alignment orientation of the part; and a program generation unit to generate a program of a robot based on the path group.

A system for analyzing geometric properties for an object includes designing the object in a first computer process and producing information relating to the geometric properties of the object, and receiving the information in a second computer processor which identifies a first portion of the geometric property information as masked or private and second portion identified as public or shared, analysis is performed by the second processor on the public/shared portion of the geometric property information. An output based on the analysis may be provided to an industrial system performing processes on the object. A binary privacy label may be assigned to each triangle in a set of triangles representing the surfaces of the object in a 3D object mesh. The privacy label denotes an associated triangle as being private or shared. The system may be used to produce a set of planned grasps for a robotic gripper.

An apparatus including a combination possibility calculation unit to calculate a stable orientation in which, from three-dimensional shape data of a part, the part is stabilized on a flat surface, to calculate a grasping method for grasping the part with a hand, and to calculate a combination in which the hand does not interfere from system configuration data including information on a connection destination of the hand and a combination group of the grasping method and the stable orientation; a regrasping path calculation unit to calculate a regrasping path of the part by using the calculated combination; a path group calculation unit to calculate a path having the minimum number of teaching points from the regrasping path as a path group based on orientation data for designating an input orientation and an alignment orientation of the part; and a program generation unit to generate a program of a robot based on the path group.

Embodiments are generally directed to generating a training dataset of labelled examples of sensor images and grasp configurations using a set of three-dimensional (3D) models of objects, one or more analytic mechanical representations of either or both of grasp forces and grasp torques, and statistical sampling to model uncertainty in either or both sensing and control. Embodiments can also include using the training dataset to train a function approximator that takes as input a sensor image and returns data that is used to select grasp configurations for a robot grasping or targeting mechanism.

Systems and methods for optimizing body and object interactions are provided. Based on obtained contact pressure maps and coefficient of friction (COF) maps at a contact interface where at least a portion of a body is in physical contact with a surface of an object, friction force maps can be determined, which can be used to optimize body and object interactions.

A grasping robot includes: a grasping mechanism configured to grasp a target object; an image-pickup unit configured to shoot a surrounding environment; an extraction unit configured to extract a graspable part that can be grasped by the grasping mechanism in the surrounding environment by using a learned model that uses an image acquired by the image-pickup unit as an input image; a position detection unit configured to detect a position of the graspable part; a recognition unit configured to recognize a state of the graspable part by referring to a lookup table that associates the position of the graspable part with a movable state thereof; and a grasping control unit configured to control the grasping mechanism so as to displace the graspable part in accordance with the state of the graspable part recognized by the recognition unit.

According to one embodiment, a selection device selects an acquisition method of a grip point for gripping an object. Based on object data corresponding to a characteristic of the object, the selection device selects one of a first method or a second method. In the first method, the selection device calculates exterior shape data of the object from an image of the object, and calculates the grip point by using the exterior shape data. In the second method, the selection device inputs the image to a first model that is trained and acquires the grip point output from the first model.

A method and computing system for object detection are presented. The computing system is configured to receive first image information representing at least a first portion of an object structure of an object in a camera's field of view, wherein the first image information is associate with a first camera pose; generate or update, based on the first image information, sensed structure information representing the object structure; identify an object corner associated with the object structure; cause the robot arm to move the camera to a second camera pose pointed at the object corner; receive second image information associated with the second camera pose; update the sensed structure information based on the second image information; determine, based on the updated sensed structure information, an object type associated with the object; determine one or more robot interaction locations based on the object type.

A system for analyzing geometric properties for an object includes designing the object in a first computer process and producing information relating to the geometric properties of the object, and receiving the information in a second computer processor which identifies a first portion of the geometric property information as masked or private and second portion identified as public or shared, analysis is performed by the second processor on the public/shared portion of the geometric property information. An output based on the analysis may be provided to an industrial system performing processes on the object. A binary privacy label may be assigned to each triangle in a set of triangles representing the surfaces of the object in a 3D object mesh. The privacy label denotes an associated triangle as being private or shared. The system may be used to produce a set of planned grasps for a robotic gripper.