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Byron E Boots, 44Seattle, WA

Known as:
Byron Emereth Boots Byron E Morgenstern Alice Lunkwicz
Related to:
Byron C Gower Jamie Morgenstern

Byron Boots Phones & Addresses

Seattle, WA   

Winthrop, WA   

Atlanta, GA   

Pittsburgh, PA   

Nashua, NH   

New Canaan, CT   

Chapel Hill, NC   

6200 Sand Point Way NE APT 203, Seattle, WA 98115   

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Publications & IP owners

Us Patents

Model Predictive Control Techniques For Autonomous Systems

US Patent:
2021033, Oct 28, 2021
Filed:
Apr 28, 2020
Appl. No.:
16/860486
Inventors:
- Santa Clara CA, US
Adam Harper Fishman - Seattle WA, US
Dieter Fox - Seattle WA, US
Byron Boots - Seattle WA, US
Fabio Tozeto Ramos - Seattle WA, US
International Classification:
G06N 3/04
G06N 5/04
G06N 3/063
G06F 17/18
G06K 9/62
G05D 1/00
Abstract:
Apparatuses, systems, and techniques to infer a sequence of actions to perform using one or more neural networks trained, at least in part, by optimizing a probability distribution function using a cost function, wherein the probability distribution represents different sequences of actions that can be performed. In at least one embodiment, a model predictive control problem is formulated as a Bayesian inference task to infer a set of solutions.

Force Estimation Using Deep Learning

US Patent:
2020030, Sep 24, 2020
Filed:
Mar 19, 2019
Appl. No.:
16/358485
Inventors:
- Santa Clara CA, US
Byron Boots - Seattle WA, US
Dieter Fox - Seattle WA, US
Ankur Handa - Seattle WA, US
Nathan Ratliff - Seattle WA, US
Balakumar Sundaralingam - Seattle WA, US
Alexander Lambert - Atlanta GA, US
International Classification:
G06F 3/01
G01L 5/22
G06N 3/08
G06T 11/00
Abstract:
A computer system generates a tactile force model for a tactile force sensor by performing a number of calibration tasks. In various embodiments, the calibration tasks include pressing the tactile force sensor while the tactile force sensor is attached to a pressure gauge, interacting with a ball, and pushing an object along a planar surface. Data collected from these calibration tasks is used to train a neural network. The resulting tactile force model allows the computer system to convert signals received from the tactile force sensor into a force magnitude and direction with greater accuracy than conventional methods. In an embodiment, force on the tactile force sensor is inferred by interacting with an object, determining the motion of the object, and estimating the forces on the object based on a physical model of the object.

Possible Relatives

David L Boots  Deborah L Boots  Dennis L Boots  Douglas R Boots  Eric N Boots  George C Boots  Gerald L Boots  Byron J Johnson  Byron L Johnston  Byron E Stanford 

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