Abstract: The method for increasing the accuracy of grasping an object can include: labelling an image based on an attempted object grasp by a robot and generating a trained graspability network using the labelled images. The method can additionally or alternatively include determining a grasp point using the trained graspability network; executing an object grasp at the grasp point S400; and/or any other suitable elements.

Abstract: Systems and method for grasp execution using height maps.

Abstract: A method for object grasping, including: determining features of a scene; determining candidate grasp locations; determining a set of candidate grasp proposals for the candidate grasp locations; optionally modifying a candidate grasp proposal of the set; determining grasp scores associated with the candidate grasp proposals; selecting a set of final grasp proposals based on the grasp scores; and executing a grasp proposal from the set of final grasp proposals.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating grasps for objects within a container. One of the methods includes determining a set of grasp proposals with associated grasping windows, wherein each grasp proposal has a different respective position within a workspace. A respective set of waypoints is determined for each grasp proposal, each set comprising a pre-grasp pose and a grasp pose within the workspace. A final selected grasp proposal is used to control an end effector of a robot to grasp an object in the workspace based on a calculated grasp trajectory associated to the selected grasp proposal.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing planning for robotic placement tasks. One of the methods includes receiving a request for a robot to perform a placement task with an object, wherein the placement task has an associated insertion plane, wherein the request specifies a location on the insertion plane and an orientation angle for the object. A plan is generated for the robot including generating a rotational motion to transition the gripped object from an initial orientation to an orientation in which a principal vector is aligned with the insertion plane and the object is aligned with the orientation angle of the request.

Abstract: The method for increasing the accuracy of grasping an object can include: labelling an image based on an attempted object grasp by a robot and generating a trained graspability network using the labelled images. The method can additionally or alternatively include determining a grasp point using the trained graspability network; executing an object grasp at the grasp point S400; and/or any other suitable elements.

Abstract: The method for increasing the accuracy of grasping an object can include: labelling an image based on an attempted object grasp by a robot and generating a trained graspability network using the labelled images. The method can additionally or alternatively include determining a grasp point using the trained graspability network; executing an object grasp at the grasp point S400; and/or any other suitable elements.

Abstract: A method for generating data explanations in a recursive cortical network includes receiving a set of evidence data at child feature nodes of a first layer of the recursive cortical network, setting a transformation configuration that directs messaging of evidence data and transformed data between layers of the network, performing a series of transformations on the evidence data according to the transformation configuration, the series including at least one forward transformation and at least one reverse transformation, and outputting the transformed evidence data.

Abstract: A method for object grasping can include: generating a set of keypoints for one or more detected objects in a scene; subdividing the set of keypoints into subsets, each corresponding to a subregion of a detected object; determining a graspability score for the subregion; determining a grasp location for the subregion; selecting a candidate grasp location; and optionally grasping an object using the candidate grasp location.

Abstract: A method for object grasping can include: generating a set of keypoints for one or more detected objects in a scene; subdividing the set of keypoints into subsets, each corresponding to a subregion of a detected object; determining a graspability score for the subregion; determining a grasp location for the subregion; selecting a candidate grasp location; and optionally grasping an object using the candidate grasp location.

Abstract: A method for object grasping can include: generating a set of keypoints for one or more detected objects in a scene; subdividing the set of keypoints into subsets, each corresponding to a subregion of a detected object; determining a graspability score for the subregion; determining a grasp location for the subregion; selecting a candidate grasp location; and optionally grasping an object using the candidate grasp location.

Abstract: The method for increasing the accuracy of grasping an object can include: labelling an image based on an attempted object grasp by a robot and generating a trained graspability network using the labelled images. The method can additionally or alternatively include determining a grasp point using the trained graspability network; executing an object grasp at the grasp point S400; and/or any other suitable elements.

Abstract: Systems and method for grasp execution using height maps.

Abstract: A method for generating data explanations in a recursive cortical network includes receiving a set of evidence data at child feature nodes of a first layer of the recursive cortical network, setting a transformation configuration that directs messaging of evidence data and transformed data between layers of the network, performing a series of transformations on the evidence data according to the transformation configuration, the series including at least one forward transformation and at least one reverse transformation, and outputting the transformed evidence data.

Abstract: A method for generating data explanations in a recursive cortical network includes receiving a set of evidence data at child feature nodes of a first layer of the recursive cortical network, setting a transformation configuration that directs messaging of evidence data and transformed data between layers of the network, performing a series of transformations on the evidence data according to the transformation configuration, the series including at least one forward transformation and at least one reverse transformation, and outputting the transformed evidence data.

Abstract: A method for generating data explanations in a recursive cortical network includes receiving a set of evidence data at child feature nodes of a first layer of the recursive cortical network, setting a transformation configuration that directs messaging of evidence data and transformed data between layers of the network, performing a series of transformations on the evidence data according to the transformation configuration, the series including at least one forward transformation and at least one reverse transformation, and outputting the transformed evidence data.