18420267. On-Robot Data Collection simplified abstract (GOOGLE LLC)
Contents
On-Robot Data Collection
Organization Name
Inventor(s)
Ammar Husain of Palo Alto CA (US)
On-Robot Data Collection - A simplified explanation of the abstract
This abstract first appeared for US patent application 18420267 titled 'On-Robot Data Collection
The patent application describes systems and methods for improving the generation and selection of robot sensor data for manual annotation and training machine learning models used in robot operation.
- On-robot controller can identify cross-modal inconsistencies, failed target tasks, or low confidence in model outputs to determine sensor data for human annotation or model updating.
- Embedding vectors representing selected sensor data can be determined and transmitted to the robot for comparison with sensor data embeddings.
- If the embeddings are similar, the sensor data can be sent to a remote system for annotation or model updating.
Potential Applications: - Robotics industry for enhancing robot operation efficiency and accuracy. - Automation processes requiring machine learning models for decision-making.
Problems Solved: - Streamlining the process of selecting sensor data for manual annotation and model training. - Improving the accuracy and effectiveness of machine learning models used in robot operation.
Benefits: - Increased efficiency in robot operation. - Enhanced accuracy in decision-making processes. - Reduction in manual labor for data annotation.
Commercial Applications: - Robotics companies looking to optimize their robot operation processes. - Automation industries seeking to improve the performance of their machine learning models.
Prior Art: - Researchers in the field of robotics and machine learning may have explored similar methods for selecting sensor data for model training.
Frequently Updated Research: - Stay updated on advancements in machine learning algorithms for robot operation. - Monitor developments in sensor data selection techniques for training machine learning models.
Questions about the Technology: 1. How does this technology improve the efficiency of robot operation processes? 2. What are the key factors considered in selecting sensor data for manual annotation and model updating?
Original Abstract Submitted
Systems and methods are provided for improved generation and selection of robot sensor data for manual annotation and/or use in training machine learning models used to operate robots. An on-robot controller can operate to determine a cross-modal inconsistency, that a temporally proximate target task was failed, and/or that a confidence in a model output indicate that particular sensor data should be transmitted to a remote system for human annotation and/or use in updating the machine learning model(s) of the robot. Embedding vector(s) representing such selected sensor data (e.g., representing common aspects across a population of sets of sensor data) could also be determined and transmitted to the robot. The robot could then determine embeddings for sensor data and, if the embeddings are similar enough to the transmitted embedding(s), the sensor data could be transmitted to the remote system for annotation and/or model updating.
