Google llc (20240100693). USING EMBEDDINGS, GENERATED USING ROBOT ACTION MODELS, IN CONTROLLING ROBOT TO PERFORM ROBOTIC TASK simplified abstract

From WikiPatents
Jump to navigation Jump to search

USING EMBEDDINGS, GENERATED USING ROBOT ACTION MODELS, IN CONTROLLING ROBOT TO PERFORM ROBOTIC TASK

Organization Name

google llc

Inventor(s)

Daniel Ho of Fremont CA (US)

Eric Jang of Cupertino CA (US)

Mohi Khansari of San Carlos CA (US)

Yu Qing Du of Berkeley CA (US)

Alexander A. Alemi of Kissimmee FL (US)

USING EMBEDDINGS, GENERATED USING ROBOT ACTION MODELS, IN CONTROLLING ROBOT TO PERFORM ROBOTIC TASK - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240100693 titled 'USING EMBEDDINGS, GENERATED USING ROBOT ACTION MODELS, IN CONTROLLING ROBOT TO PERFORM ROBOTIC TASK

Simplified Explanation

The patent application describes using trained robotic action ML models to control a robot in performing tasks. This includes a first modality robotic action ML model and a second modality robotic action ML model that generate predicted action outputs based on sensor data. The weights for each pair of predicted action outputs are dynamically determined based on embeddings, and a final predicted action output is used to control the robot.

  • The patent application involves using trained robotic action ML models to control a robot in performing tasks.
  • It includes a first modality robotic action ML model and a second modality robotic action ML model that generate predicted action outputs based on sensor data.
  • The weights for each pair of predicted action outputs are dynamically determined based on embeddings.
  • A final predicted action output is used to control the robot.

Potential Applications

The technology described in the patent application could be applied in various industries and fields, including:

  • Manufacturing
  • Healthcare
  • Agriculture
  • Logistics

Problems Solved

This technology helps in addressing several challenges, such as:

  • Improving efficiency in task performance
  • Enhancing precision and accuracy in robotic actions
  • Streamlining operations in various industries

Benefits

The use of trained robotic action ML models offers several benefits, including:

  • Increased productivity
  • Reduced errors in task execution
  • Enhanced safety in robotic operations

Potential Commercial Applications

The technology has potential commercial applications in:

  • Robotic automation systems
  • Industrial robotics
  • Autonomous vehicles
  • Smart home devices

Possible Prior Art

One possible prior art could be the use of traditional robotic control systems that do not incorporate ML models for predicting actions based on sensor data.

Unanswered Questions

How does the dynamic determination of weights based on embeddings improve the performance of the robotic system?

The dynamic determination of weights based on embeddings helps in optimizing the control of the robot by adjusting the influence of each predicted action output according to the context of the task and the sensor data. This can lead to more accurate and efficient robotic actions.

What are the potential limitations or challenges in implementing this technology in real-world robotic systems?

Some potential limitations or challenges in implementing this technology could include the complexity of training and deploying multiple ML models, the need for robust sensor data processing, and the integration of the dynamic weight determination mechanism into existing robotic control systems. Addressing these challenges effectively will be crucial for the successful adoption of this technology in practical applications.


Original Abstract Submitted

some implementations relate to using trained robotic action ml models in controlling a robot to perform a robotic task. some versions of those implementations include (a) a first modality robotic action ml model that is used to generate, based on processing first modality sensor data instances, first predicted action outputs for the robotic task and (b) a second modality robotic action ml model that is used to generate, in parallel and based on processing second modality sensor data instances, second predicted action outputs for the robotic task. in some of those versions, respective weights for each pair of the first and second predicted action outputs are dynamically determined based on analysis of embeddings generated in generating the first and second predicted action outputs. a final predicted action output, for controlling the robot, is determined based on the weights.

Retrieved from "http://wikipatents.org/index.php?title=Google_llc_(20240100693)._USING_EMBEDDINGS,_GENERATED_USING_ROBOT_ACTION_MODELS,_IN_CONTROLLING_ROBOT_TO_PERFORM_ROBOTIC_TASK_simplified_abstract&oldid=49372"