University of California

Siemens Aktiengesellschaft

Jeffrey Brian Mahler

Kenneth Y Goldberg

Juan L Aparicio Ojea

Ramu Sharat Chandra

Ken Goldberg

Matthew Matl

Florian Till Pokorny

Ramu Chandra

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.

Robotic systems and methods for robustly grasping and targeting objects

A system for analyzing geometric properties for an object includes designing the object in a first computer process and producing information relating to the geometric properties of the object, and receiving the information in a second computer processor which identifies a first portion of the geometric property information as masked or private and second portion identified as public or shared, analysis is performed by the second processor on the public/shared portion of the geometric property information. An output based on the analysis may be provided to an industrial system performing processes on the object. A binary privacy label may be assigned to each triangle in a set of triangles representing the surfaces of the object in a 3D object mesh. The privacy label denotes an associated triangle as being private or shared. The system may be used to produce a set of planned grasps for a robotic gripper.

Method and system for preserving privacy for cloud-based manufacturing analysis services

Growing community of inventors

Berkeley, CA, United States of America

Jeffrey Brian Mahler