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: A method for inferring patterns in multi-dimensional image data comprises providing a recursive network of sub-networks with a parent feature node and at least two child feature nodes; wherein each sub-network is associated with a distinct subset of the space; configuring nodes of the sub-networks with posterior distribution component; receiving image data feature input at the final child feature nodes; propagating node activation through the network layer hierarchy in a manner consistent with node connections of sub-networks of the network and the posterior prediction of child nodes; and outputting parent feature node selection to an inferred output.

Abstract: A method for inferring patterns in multi-dimensional image data comprises providing a recursive network of sub-networks with a parent feature node and at least two child feature nodes; wherein each sub-network is associated with a distinct subset of the space; configuring nodes of the sub-networks with posterior distribution component; receiving image data feature input at the final child feature nodes; propagating node activation through the network layer hierarchy in a manner consistent with node connections of sub-networks of the network and the posterior prediction of child nodes; and outputting parent feature node selection to an inferred output.

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: A system includes: a recursively architected network of sub-networks organized into a hierarchical layers; the sub-networks including at least a parent feature node, a pool node, a parent-specific child feature (PSCF) node, and a child feature node; the parent feature node of at least one sub-network configured with a selection function actionable on at least two pool nodes connected to the parent feature node of the at least one sub-network; the pool node of the at least one sub-network configured with a selection function actionable on at least two PSCF nodes connected to the pool node of the at least one sub-network; the PSCF node of the at least one sub-network configured to activate a connected child feature node; the child feature node connectable to at least a parent feature node of a sub-network at a lower hierarchical layer.

Abstract: A method for inferring patterns in multi-dimensional image data comprises providing a recursive network of sub-networks with a parent feature node and at least two child feature nodes; wherein each sub-network is associated with a distinct subset of the space; configuring nodes of the sub-networks with posterior distribution component; receiving image data feature input at the final child feature nodes; propagating node activation through the network layer hierarchy in a manner consistent with node connections of sub-networks of the network and the posterior prediction of child nodes; and outputting parent feature node selection to an inferred output.

Abstract: A system includes: a recursively architected network of sub-networks organized into a hierarchical layers; the sub-networks including at least a parent feature node, a pool node, a parent-specific child feature (PSCF) node, and a child feature node; the parent feature node of at least one sub-network configured with a selection function actionable on at least two pool nodes connected to the parent feature node of the at least one sub-network; the pool node of the at least one sub-network configured with a selection function actionable on at least two PSCF nodes connected to the pool node of the at least one sub-network; the PSCF node of the at least one sub-network configured to activate a connected child feature node; the child feature node connectable to at least a parent feature node of a sub-network at a lower hierarchical layer.

Abstract: A method for generating patterns with a network includes providing a recursive network of sub-networks with a parent feature node and at least two child feature nodes; and at a first sub-network of the recursive network, the first sub-network including the parent feature node and the at least two child feature nodes, selecting a first pool node and a second pool node consistent with a selection function of the parent feature node, selecting at least a first parent-specific child feature (PSCF) node that corresponds to a first child feature node of the sub-network, selecting at least a second parent-specific child feature (PSCF) node that corresponds to a second child feature node of the sub-network; and compiling the state of final child feature nodes, including the first and second child feature nodes, of the network into a generated output.

Abstract: A method for generating patterns with a network includes providing a recursive network of sub-networks with a parent feature node and at least two child feature nodes; and at a first sub-network of the recursive network, the first sub-network including the parent feature node and the at least two child feature nodes, selecting a first pool node and a second pool node consistent with a selection function of the parent feature node, selecting at least a first parent-specific child feature (PSCF) node that corresponds to a first child feature node of the sub-network, selecting at least a second parent-specific child feature (PSCF) node that corresponds to a second child feature node of the sub-network; and compiling the state of final child feature nodes, including the first and second child feature nodes, of the network into a generated output.

Abstract: A system includes: a recursively architected network of sub-networks organized into a hierarchical layers; the sub-networks including at least a parent feature node, a pool node, a parent-specific child feature (PSCF) node, and a child feature node; the parent feature node of at least one sub-network configured with a selection function actionable on at least two pool nodes connected to the parent feature node of the at least one sub-network; the pool node of the at least one sub-network configured with a selection function actionable on at least two PSCF nodes connected to the pool node of the at least one sub-network; the PSCF node of the at least one sub-network configured to activate a connected child feature node; the child feature node connectable to at least a parent feature node of a sub-network at a lower hierarchical layer.

Abstract: A system includes: a recursively architected network of sub-networks organized into a hierarchical layers; the sub-networks including at least a parent feature node, a pool node, a parent-specific child feature (PSCF) node, and a child feature node; the parent feature node of at least one sub-network configured with a selection function actionable on at least two pool nodes connected to the parent feature node of the at least one sub-network; the pool node of the at least one sub-network configured with a selection function actionable on at least two PSCF nodes connected to the pool node of the at least one sub-network; the PSCF node of the at least one sub-network configured to activate a connected child feature node; the child feature node connectable to at least a parent feature node of a sub-network at a lower hierarchical layer.

Abstract: A system and method for generating and inferring patterns with a network that includes providing a network of recursive sub-networks with a parent feature input node and at least two child feature output nodes; propagating node selection through the network layer hierarchy in a manner consistent with node connections of sub-networks of the network, the propagation within the sub-network including enforcing a selection constraint on at least a second node of a second pool according to a constraint node of the sub-network; and compiling the state of final child feature nodes of the network into a generated output.

Abstract: A computer-implemented method for object recognition using a recursive cortical network comprising receiving an input image at an input module, applying a trained recursive cortical network (RCN) to the image using an inference module to activate child features of the RCN, selecting pools of the RCN containing the activated child features, propagating the selection of the pools to identify probabilities of one or more high-level features matching one or more objects in the input image.

Abstract: A low spatial frequency video sequence is enhanced to provide a higher spatial frequency video sequence using a super-resolution process. Patches of higher frequency image data are inferred from the images of the lower frequency video sequence and a dictionary containing a training set of higher resolution image data. An inference module selects result patches from the training set to preserve spatial consistency within each image frame and to preserve temporal consistency, between image frames of the resulting video sequence. Temporal consistency can be preserved for static portions and/or moving portions of each frame.

Abstract: A low spatial frequency video sequence is enhanced to provide a higher spatial frequency video sequence using a super-resolution process. Patches of higher frequency image data are inferred from the images of the lower frequency video sequence and a dictionary containing a training set of higher resolution image data. An inference module selects result patches from the training set to preserve spatial consistency within each image frame and to preserve temporal consistency, between image frames of the resulting video sequence. Temporal consistency can be preserved for static portions and/or moving portions of each frame.