Amazon Technologies, Inc.

Benjamin Lev Snyder

Brian C Beckman

Brandon William Porter

Corrinne Yu

Michael Vogelsong

Leonardo Ruggiero Bachega

James Stevenson

Aritra Biswas

Daniel Patrick Weaver

Simone Elviretti

Rasika Sanjay Jangle

Liliane Jeanne Barbour

Paul Hercules Mandac Rivera

A machine learning system builds and uses computer models for controlling robotic performance of a task. Such computer models may be first trained using feedback on computer simulations of the robotic system performing the task, and then refined using feedback on real-world trials of the robot performing the task. Some examples of the computer models can be trained to automatically evaluate robotic task performance and provide the feedback. This feedback can be used by a machine learning system, for example an evolution strategies system or reinforcement learning system, to generate and refine the controller.

Simulation-real world feedback loop for learning robotic control policies

In response to a programmatic interaction, respective representations of items of an initial result set are presented to an item consumer. One or more result refinement iterations are then conducted. In a given iteration, one or more feedback indicators with respect to one or more items are identified, a machine learning model is trained using at least the feedback indicators to generate respective result set candidacy metrics for at least some items, and the metrics are then used to transmit additional items for presentation to the item consumer.

Artificial intelligence system for real-time visual feedback-based refinement of query results

A machine learning system builds and uses computer models for controlling robotic performance of a task. Such computer models may be first trained using feedback on computer simulations of the robot performing the task, and then refined using feedback on real-world trials of the robot performing the task. Some examples of the computer models can be trained to automatically evaluate robotic task performance and provide the feedback. This feedback can be used by a machine learning system, for example an evolution strategies system or reinforcement learning system, to generate and refine the controller.

Artificial intelligence system for learning robotic control policies

A machine learning system builds and uses computer models for identifying how to evaluate the level of success reflected in a recorded observation of a task. Such computer models may be used to generate a policy for controlling a robotic system performing the task. The computer models can also be used to evaluate robotic task performance and provide feedback for recalibrating the robotic control policy.

Artificial intelligence system for modeling and evaluating robotic success at task performance

Combined body and foot massager

Growing community of inventors

Seattle, WA, United States of America

Benjamin Lev Snyder