Perrone Robotics Collaborates With Machine Learning Research Pioneers on Autonomous Perception
CROZET, Va., — Perrone Robotics Inc. (PRI), a Virginia-based robotics company today announced the company is collaborating with Professor Robert Hecht-Nielsen of the University of California, San Diego's (UCSD) Vertebrate Movement Laboratory (VML) and its research team on advanced machine learning methods for autonomous vehicle perception and control.
The new collaboration project is based on a groundbreaking method for perception and machine learning for autonomous vehicles and will combine Hecht-Nielsen's work on artificial neural networks (ANN), confabulation theory, and vertebrate movement mathematics with PRI's applied experience in autonomous vehicles and robots.
"From the earliest days of our MAX platform, we have anticipated and designed in support for machine learning and AI," explained Paul Perrone, CEO and founder of Perrone Robotics. "But, this collaboration with the UCSD team will completely extend our existing support. Dr. Hecht-Nielsen has very novel and powerful ideas that we believe will compel the entire industry to move forward and, when we successfully harness these concepts, users of the Perrone platform will leverage state of the art for machine learning easily and apply them to their existing solutions," Perrone added.
The project's intended outcome is a new framework for PRI's patented MAX platform that will apply innovative learning techniques to MAX-based applications, specifically in the driverless car space.
Perrone's MAX platform contains services for collecting data from sensors, making decisions, and taking actions via controls. It includes a UGV (Unmanned Guided Vehicle) layer provides path and movement planning services suitable for any kind of robot (or self-driving car). Additionally, the MAX-Auto layer provides automotive maneuvers such as stopping at lights, merging on highways, and handling traffic circles, among other functions.
Perrone Robotics will have exclusive access to this project and use it to implement highly competent control of driverless vehicles for automobile, truck, and other ground vehicles.
"We've been interested in Perrone's work with autonomy for some time; Paul and his team have proven that the MAX platform can be applied in multiple domains to effectively control robotic vehicles," said Professor Hecht-Nielsen. "We are very excited to work with the Perrone team to make these good solutions even better by applying the insights and techniques our team has developed and we expect to see enhanced autonomy through improved decision-making, perception, and finer-grained control of a given platform."
Today, the machine learning algorithms are extensively used to find the solutions to various challenges faced by self-driving cars.
As part of the collaboration, the UCSD VML research team will publish new research that is expected to start a major new trend in the study of machine intelligence.
Work on the new platform will continue through 2018 and into 2020.
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