IDiyas

For the Inventor, By the Inventor

Fremont, CA, United States of America

Yunfei Bai

USPTO Granted Patents = 28 

 

Average Co-Inventor Count = 2.4

ph-index = 4

Forward Citations = 52(Granted Patents)


Location History:

  • San Jose, CA (US) (2019)
  • Sunnyvale, CA (US) (2020 - 2022)
  • Union City, CA (US) (2020 - 2022)
  • Fremont, CA (US) (2020 - 2024)

Company Filing History:

goldMedal17 out of 32,429 
 
Google Inc.
 patents
silverMedal8 out of 1,194 
 
X Development LLC
 patents
bronzeMedal2 out of 6,898 
 
Adobe, Inc.
 patents
4. 1 out of 12 
 
Gdm Holding LLC
 patents

Years Active: 2019-2025

Loading Chart...
Loading Chart...
28 patents (USPTO):
1. 12472630 - Simulation driven robotic control of real robot(s) (2025-11-18),
2. 12226920 - System(s) and method(s) of using imitation learning in training and refining robotic control policies (2025-02-18),
3. 12172309 - Systems and methods for learning agile locomotion for multiped robots (2024-12-24),
4. 12165347 - Robotic control based on 3D bounding shape, for an object, generated using edge-depth values for the object (2024-12-10),
5. 12138793 - Machine learning methods and apparatus for robotic manipulation and that utilize multi-task domain adaptation (2024-11-12),
6. 12112494 - Robotic manipulation using domain-invariant 3D representations predicted from 2.5D vision data (2024-10-08),
7. 12083678 - Efficient adaption of robot control policy for new task using meta-learning based on meta-imitation learning and meta-reinforcement learning (2024-09-10),
8. 12079308 - Mitigating reality gap through modification of simulated state data of robotic simulator (2024-09-03),
9. 11938638 - Simulation driven robotic control of real robot(s) (2024-03-26),
10. 11887363 - Training a deep neural network model to generate rich object-centric embeddings of robotic vision data (2024-01-30),
11. 11833661 - Utilizing past contact physics in robotic manipulation (e.g., pushing) of an object (2023-12-05),
12. 11823048 - Generating simulated training examples for training of machine learning model used for robot control (2023-11-21),
13. 11790042 - Mitigating reality gap through modification of simulated state data of robotic simulator (2023-10-17),
14. 11772272 - System(s) and method(s) of using imitation learning in training and refining robotic control policies (2023-10-03),
15. 11707840 - Mitigating reality gap through optimization of simulated hardware parameter(s) of simulated robot (2023-07-25),

Title: Innovations in Robotics: The Work of Yunfei Bai

Introduction: Yunfei Bai is a prominent inventor based in Fremont, CA, who has made significant contributions to the field of robotics through his extensive research and innovative patents. With 27 patents to his name, he is recognized for his pioneering work in robotic control policies and agile locomotion.

Latest Patents: Among his most recent inventions is a system and method for using imitation learning in training and refining robotic control policies. This invention involves training robots based on human demonstrations and refining their performance through human interventions. The method includes the capability of the robotic control policy to assess its performance and alert a human to intervene if necessary, enhancing the efficiency and safety of robotic tasks. Additionally, Yunfei holds a patent for systems and methods for learning agile locomotion for multiped robots, where he utilizes a decoupled machine learning model that allows users to define desired trajectories for improved locomotion control.

Career Highlights: Throughout his career, Yunfei Bai has collaborated with influential technology companies such as Google Inc. and X Development LLC. His work has focused on leveraging advanced machine learning techniques to enhance robotics applications, demonstrating a commitment to practical and efficient solutions in this rapidly evolving field.

Collaborations: Yunfei has worked alongside esteemed colleagues including Seyed Mohammad Khansari Zadeh and Mrinal Kalakrishnan. Together, they have explored innovative methodologies that drive advancements in robotic technology, further establishing Yunfei's reputation as a leader in this sector.

Conclusion: Yunfei Bai's contributions to robotic innovations exemplify the intersection of creativity and technology. His 27 patents reflect a significant commitment to improving robotic functionality, ensuring these machines can learn from and adapt to human interaction and guidance. As robotics continue to evolve, the impact of Yunfei Bai's work will undoubtedly shape future developments in this exciting field.

This text is generated by artificial intelligence and may not be accurate.
Please report any incorrect information to support@idiyas.com
Loading…