Perrone Robotics, Inc. (“PRI”) 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.
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 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.
Project Based on Decade of Research; Challenges Existing Notions of Decision-Making
Over the past 10 years, research carried out by the members of Hecht-Nielsen’s UCSD lab has challenged traditional neuroscience explanations for neuronal computations involved in vertebrate movement. Today’s standard human neuroscience claims that neuronal calculations required for making human movements are carried out almost entirely in the brain.
The VML team’s observed data show that almost all of the neuronal calculations occur within sets of neurons within the spinal column. Further, these calculations take on a mathematical form that is entirely different, and completely incompatible with “Deep Learning” approaches that current automotive AI researchers use.
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.
Source: Perrone Robotics