An information processing apparatus comprises an obtaining unit configured to obtain sensing data on an area including an installation target component; a detection unit configured to detect a position/orientation of the installation target component as a component position/orientation based on the sensing data; a setting unit configured to set, based on the component position/orientation and shape data on the installation target component, a candidate gripped portion of the installation target component to be gripped by a hand mechanism; a calculation unit configured to calculate, as a candidate hand position/orientation, a position/orientation of the hand mechanism in which the candidate gripped portion can be gripped; and a generation unit configured to generate candidate teaching data for gripping operation by the hand mechanism by associating the candidate gripped portion and the candidate hand position/orientation with each other.

A region-based robotic grasp generation technique for machine tending or bin picking applications. Part and gripper geometry are provided as inputs, typically from CAD files, along with gripper kinematics. A human user defines one or more target grasp regions on the part, using a graphical user interface displaying the part geometry. The target grasp regions are identified by the user based on the user's knowledge of how the part may be grasped to ensure that the part can be subsequently placed in a proper destination pose. For each of the target grasp regions, an optimization solver is used to compute a plurality of quality grasps with stable surface contact between the part and the gripper, and no part-gripper interference. The computed grasps for each target grasp region are placed in a grasp database which is used by a robot in actual bin picking operations.

A system and method for motion planning is presented. The system is configured, when an object is or has been in a camera field of view of a camera, to receive first image information that is generated when the camera has a first camera pose. The system is further configured to determine, based on the first image information, a first estimate of the object structure, and to identify, based on the first estimate of the object structure or based on the first image information, an object corner. The system is further configured to cause an end effector apparatus to move the camera to a second camera pose, and to receive second image information for representing the object's structure. The system is configured to determine a second estimate of the object's structure based on the second image information, and to generate a motion plan based on at least the second estimate.

A control device comprises a first interface, a second interface, a processor, and a third interface. A first interface is configured to acquire a captured image of an article. A second interface is configured to transmit and receive data to and from an input/output device. A processor is configured to cause the input/output device to display an article image based on the captured image, receive an input of a position and an angle of a grip portion model of a grip portion that grips the article from the input/output device through the second interface, display the grip portion model on the article image, and generate a gripping plan. A third interface configured to transmit the gripping plan to a control unit of a gripping device including the grip portion.

A system and method for motion planning is presented. The system is configured, when an object is or has been in a camera field of view of a camera, to receive first image information that is generated when the camera has a first camera pose. The system is further configured to determine, based on the first image information, a first estimate of the object structure, and to identify, based on the first estimate of the object structure or based on the first image information, an object corner. The system is further configured to cause an end effector apparatus to move the camera to a second camera pose, and to receive second image information for representing the object's structure. The system is configured to determine a second estimate of the object's structure based on the second image information, and to generate a motion plan based on at least the second estimate.

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.

An information processor calculates, for a robot hand including a plurality of fingers, a gripping pose at which the robot hand grips a target object. The information processor includes a candidate single-finger placement position detector that detects, based on three-dimensional measurement data obtained through three-dimensional measurement of the target object and hand shape data about a shape of the robot hand, candidate placement positions for each of the plurality of fingers of the robot hand, a multi-finger combination searcher that searches for, among the candidate placement positions for each of the plurality of fingers, a combination of candidate placement positions to allow gripping of the target object, and a gripping pose calculator that calculates, based on the combination of candidate placement positions for each of the plurality of fingers, a gripping pose at which the robot hand grips the target object.

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.

A system and method for motion planning is presented. The system is configured, when an object is or has been in a camera field of view of a camera, to receive first image information that is generated when the camera has a first camera pose. The system is further configured to determine, based on the first image information, a first estimate of the object structure, and to identify, based on the first estimate of the object structure or based on the first image information, an object corner. The system is further configured to cause an end effector apparatus to move the camera to a second camera pose, and to receive second image information for representing the object's structure. The system is configured to determine a second estimate of the object's structure based on the second image information, and to generate a motion plan based on at least the second estimate.

A system and method for motion planning is presented. The system is configured, when an object is or has been in a camera field of view of a camera, to receive first image information that is generated when the camera has a first camera pose. The system is further configured to determine, based on the first image information, a first estimate of the object structure, and to identify, based on the first estimate of the object structure or based on the first image information, an object corner. The system is further configured to cause an end effector apparatus to move the camera to a second camera pose, and to receive second image information for representing the object's structure. The system is configured to determine a second estimate of the object's structure based on the second image information, and to generate a motion plan based on at least the second estimate.