Lessons from the Field: Deep Learning and Machine Perception for Field Robots

ABSTRACT:

Lessons from the Field: Deep Learning and Machine Perception for field robots - Mobile robots now deliver vast amounts of sensor data from large unstructured environments. In attempting to process and interpret this data there are many unique challenges in bridging the gap between prerecorded data sets and the field. This talk will present recent work addressing the application of machine learning techniques to mobile robotic perception. We will discuss solutions to the assessment of risk in self-driving vehicles, thermal cameras for object detection and mapping and finally object detection and grasping and manipulation in underwater contexts. Real field data will guide this process and we will show results on deployed field robotic vehicles.

BIO:

Matthew Johnson-Roberson is director of the Carnegie Mellon Robotics Institute and a Professor in the School of Computer Science. He received a PhD from the University of Sydney in 2010. He has held prior postdoctoral appointments with the Centre for Autonomous Systems - CAS at KTH Royal Institute of Technology in Stockholm and the Australian Centre for Field Robotics at the University of Sydney. He co-founded Refraction AI a last-mile autonomous vehicle delivery company. He has worked in robotic perception since the first DARPA grand challenge and his group focuses on enabling robots to better see and understand their environment.