View source for 18436684. SYSTEM AND METHODS FOR PIXEL BASED MODEL PREDICTIVE CONTROL simplified abstract (GOOGLE LLC)

=SYSTEM AND METHODS FOR PIXEL BASED MODEL PREDICTIVE CONTROL=


==Organization Name==

[[:Category:GOOGLE LLC|GOOGLE LLC]]


[[Category:GOOGLE LLC]]

==Inventor(s)==

[[:Category:Danijar Hafner of Toronto (CA)|Danijar Hafner of Toronto (CA)]][[Category:Danijar Hafner of Toronto (CA)]]

==SYSTEM AND METHODS FOR PIXEL BASED MODEL PREDICTIVE CONTROL - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18436684 titled 'SYSTEM AND METHODS FOR PIXEL BASED MODEL PREDICTIVE CONTROL

==Simplified Explanation==

The patent application describes techniques for model predictive control of a robot using a latent dynamics model and a reward function. The latent space can be divided into deterministic and stochastic portions to generate likely robot trajectories based on the model.

* The patent enables model predictive control of a robot based on a latent dynamics model and a reward function.
* The latent space can be divided into deterministic and stochastic portions for generating likely robot trajectories.
* Multiple reward functions can be used, each corresponding to a different robot task.

== Potential Applications ==
This technology could be applied in various fields such as industrial automation, autonomous vehicles, robotic surgery, and smart manufacturing.

== Problems Solved ==
1. Improved control and optimization of robot movements.
2. Enhanced efficiency and performance of robotic systems.

== Benefits ==
1. Increased accuracy and precision in robot control.
2. Enhanced adaptability to different robot tasks.
3. Improved overall productivity and operational efficiency.

== Potential Commercial Applications ==
Optimized Robot Control for Industrial Automation

== Possible Prior Art ==
Prior art related to model predictive control, latent dynamics models, and reward functions in robotics could be relevant to this patent application.

== Unanswered Questions ==
1. What specific industries or sectors could benefit the most from this technology?
2. Are there any limitations or constraints in implementing this model predictive control system in real-world robotic applications?


==Original Abstract Submitted==

Techniques are disclosed that enable model predictive control of a robot based on a latent dynamics model and a reward function. In many implementations, the latent space can be divided into a deterministic portion and stochastic portion, allowing the model to be utilized in generating more likely robot trajectories. Additional or alternative implementations include many reward functions, where each reward function corresponds to a different robot task.



[[Category:B25J9/16]]
[[Category:G06N7/01]]