Intrinsic Innovation LLC

Deepmind Technologies Limited

Vicarious Fpc, Inc.

Sivaramakrishnan Swaminathan

Michael Stark

Manushree Gangwar

Alex Schlegel

Eyrun Eyjolfsdottir

Karthikeyan Yuvaraj

Marco Carra

Miguel Lazaro-Gredilla

Lucas Whitaker

David A Mely

Dileep George

Meet Kirankumar Dave

Scott Bandy

Anna Chen

Dennis Park

Peter So

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for automatically generating object representations. One of the methods includes grasping, by a robot, an object at a first grasp point and generating a first partial object mesh based on one or more first sensor measurements of the object when held by the robot at the first grasp point. A second grasp point is identified for the object that is located in a region captured by the one or more first sensor measurements. A second partial object mesh is generated based on one or more second sensor measurements of the object when held by the robot at the second grasp point.

System and method for object detector training

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for object simulation. One of the methods includes generating a mesh representation of a deformable object and generating, from the mesh representation of the deformable object, a structural representation of the deformable object. A plurality of simulated instances of the object are generated based on kinematics of the structural representation of the deformable object.

Object simulation

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a graph model representing an environment being interacted with by an agent. In one aspect, one of the methods include: obtaining experience data; using the experience data to update a visitation count for each of one or more state-action pairs represented by the graph model; and at each of multiple environment exploration steps: computing a utility measure for each of the one or more state-action pairs represented by the graph model; determining, based on the utility measures, a sequence of one or more planned actions that have an information gain that satisfies a threshold; and controlling the agent to perform the sequence of one or more planned actions to cause the environment to transition from a state characterized by a last observation received after a last action in the experience data into a different state.

Fast exploration and learning of latent graph models

A method for generating training data can include: determining a set of images; determining a set of masks based on the images; determining a first mesh based on the set of masks; optionally determining a refined mesh by recomputing the first mesh; optionally determining one or more faces of the refined mesh; optionally adding one or more keypoints to the refined mesh; optionally determining a material property set for the object; optionally generating a full object mesh; determining one or more scenes; optionally determining training data based on the one or more scenes; optionally training one or more object detectors using the training data; and detecting one or more objects using the trained object detector.

Method and system for generating training data

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Union City, CA, United States of America

Sivaramakrishnan Swaminathan