Company Filing History:
Years Active: 2017-2020
Title: The Innovative Mind of Moslem Kazemi
Introduction
Moslem Kazemi is a prominent inventor based in San Diego, CA. He has made significant contributions to the field of robotics, particularly in the area of haptic training. With a total of 3 patents to his name, Kazemi is recognized for his innovative approaches to enhancing robotic learning processes.
Latest Patents
One of Kazemi's latest patents is focused on "Apparatus and methods for haptic training of robots." This invention allows robotic devices to be trained by a human trainer who guides the robot along a target trajectory through physical contact. The robot is equipped with an adaptive controller that generates control commands based on various inputs, including those from the trainer and sensory data. The trainer observes the robot's task execution and, upon noticing discrepancies between the target and actual behavior, provides teaching inputs through haptic actions. The robot then executes actions based on a combination of its internal control signals and the training input. This innovative approach allows the robot to adjust its learning process according to the teaching input, thereby reducing discrepancies in future trials.
Career Highlights
Kazemi is currently employed at Brain Corporation, where he continues to develop cutting-edge technologies in robotics. His work focuses on improving the interaction between humans and robots, making robotic systems more intuitive and effective in various applications.
Collaborations
Throughout his career, Kazemi has collaborated with talented individuals such as Filip Ponulak and Patryk Laurent. These collaborations have further enriched his work and contributed to the advancement of robotic technologies.
Conclusion
Moslem Kazemi's innovative contributions to robotics, particularly in haptic training, highlight his role as a leading inventor in the field. His work at Brain Corporation and his collaborations with other experts continue to push the boundaries of what is possible in robotic learning and interaction.