View source for 20240042600. DATA-DRIVEN ROBOT CONTROL simplified abstract (DeepMind Technologies Limited)

=DATA-DRIVEN ROBOT CONTROL=


==Organization Name==

[[:Category:DeepMind Technologies Limited|DeepMind Technologies Limited]]


[[Category:DeepMind Technologies Limited]]

==Inventor(s)==

[[:Category:Serkan Cabi of London (GB)|Serkan Cabi of London (GB)]][[Category:Serkan Cabi of London (GB)]]

[[:Category:Ziyu Wang of Markham (CA)|Ziyu Wang of Markham (CA)]][[Category:Ziyu Wang of Markham (CA)]]

[[:Category:Alexander Novikov of London (GB)|Alexander Novikov of London (GB)]][[Category:Alexander Novikov of London (GB)]]

[[:Category:Ksenia Konyushkova of London (GB)|Ksenia Konyushkova of London (GB)]][[Category:Ksenia Konyushkova of London (GB)]]

[[:Category:Sergio Gomez Colmenarejo of London (GB)|Sergio Gomez Colmenarejo of London (GB)]][[Category:Sergio Gomez Colmenarejo of London (GB)]]

[[:Category:Scott Ellison Reed of Atlanta GA (US)|Scott Ellison Reed of Atlanta GA (US)]][[Category:Scott Ellison Reed of Atlanta GA (US)]]

[[:Category:Misha Man Ray Denil of London (GB)|Misha Man Ray Denil of London (GB)]][[Category:Misha Man Ray Denil of London (GB)]]

[[:Category:Jonathan Karl Scholz of London (GB)|Jonathan Karl Scholz of London (GB)]][[Category:Jonathan Karl Scholz of London (GB)]]

[[:Category:Oleg O. Sushkov of London (GB)|Oleg O. Sushkov of London (GB)]][[Category:Oleg O. Sushkov of London (GB)]]

[[:Category:Rae Chan Jeong of North York (CA)|Rae Chan Jeong of North York (CA)]][[Category:Rae Chan Jeong of North York (CA)]]

[[:Category:David Barker of Reading (GB)|David Barker of Reading (GB)]][[Category:David Barker of Reading (GB)]]

[[:Category:David Budden of London (GB)|David Budden of London (GB)]][[Category:David Budden of London (GB)]]

[[:Category:Mel Vecerik of London (GB)|Mel Vecerik of London (GB)]][[Category:Mel Vecerik of London (GB)]]

[[:Category:Yusuf Aytar of London (GB)|Yusuf Aytar of London (GB)]][[Category:Yusuf Aytar of London (GB)]]

[[:Category:Joao Ferdinando Gomes De Freitas of London (GB)|Joao Ferdinando Gomes De Freitas of London (GB)]][[Category:Joao Ferdinando Gomes De Freitas of London (GB)]]

==DATA-DRIVEN ROBOT CONTROL - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240042600 titled 'DATA-DRIVEN ROBOT CONTROL

==Simplified Explanation==

Methods, systems, and apparatus are disclosed for data-driven robotic control. The patent application describes a method that involves maintaining robot experience data, obtaining annotation data, training a reward model on the annotation data, and generating task-specific training data for a particular task. The task-specific training data is generated by processing observations from a subset of the robot experience data using the trained reward model to generate reward predictions and associating the reward predictions with the corresponding experiences. A policy neural network is then trained on the task-specific training data to generate a control policy for a robot performing the particular task.

* The patent application describes a method for data-driven robotic control.
* The method involves maintaining robot experience data and obtaining annotation data.
* A reward model is trained on the annotation data.
* Task-specific training data is generated by processing observations from a subset of the robot experience data using the trained reward model.
* The reward predictions generated from the trained reward model are associated with the corresponding experiences.
* A policy neural network is trained on the task-specific training data to generate a control policy for a robot performing a particular task.

Potential applications of this technology:

* Robotic control systems that can learn from experience and adapt to different tasks.
* Automation of complex tasks in various industries such as manufacturing, logistics, and healthcare.
* Autonomous robots capable of performing a wide range of tasks without explicit programming.

Problems solved by this technology:

* Traditional robotic control methods often require manual programming for each specific task, which can be time-consuming and costly.
* Adapting robots to new tasks or environments can be challenging without a data-driven approach.
* This technology enables robots to learn from experience and improve their performance over time.

Benefits of this technology:

* Increased efficiency and productivity in industries that rely on robotic automation.
* Flexibility and adaptability of robotic systems to perform different tasks.
* Reduced need for manual programming and maintenance of robotic control systems.
* Improved performance and accuracy of robots through continuous learning and optimization.


==Original Abstract Submitted==

methods, systems, and apparatus, including computer programs encoded on computer storage media, for data-driven robotic control. one of the methods includes maintaining robot experience data; obtaining annotation data; training, on the annotation data, a reward model; generating task-specific training data for the particular task, comprising, for each experience in a second subset of the experiences in the robot experience data: processing the observation in the experience using the trained reward model to generate a reward prediction, and associating the reward prediction with the experience; and training a policy neural network on the task-specific training data for the particular task, wherein the policy neural network is configured to receive a network input comprising an observation and to generate a policy output that defines a control policy for a robot performing the particular task.



[[Category:B25J9/16]]