Vicarious Fpc, Inc.

Intrinsic Innovation LLC

Bhaskara Mannar Marthi

Dileep George

Kenneth Alan Kansky

Christopher Remmert Laan

Wolfgang Lehrach

Nan Rong

Michael Stark

D Scott Phoenix

David Scott Phoenix

Ahmad Humayun

Li Yang Ku

Eric J Purdy

Wolfang Lehrach

Aravind Sivakumar

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.

Method and system for object grasping

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 S; and/or any other suitable elements.

Method and system for grasping an object

Systems and method for grasp execution using height maps.

System and method for height-map-based grasp execution

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.

Systems and methods for generating data explanations for neural networks and related systems

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.

System and method for a recursive cortical network

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.

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.

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.

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.

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.

Growing community of inventors

Sunnyvale, CA, United States of America

Bhaskara Mannar Marthi