Roboticists Go Off Road To Compile Data That Could Train Self-Driving ATVs

But that sort of information isn’t often available and, even when it is, might not be useful. The research team found that the multimodal sensor data they gathered for TartanDrive enabled them to build prediction models superior to those developed with simpler, nondynamic data. Driving aggressively also pushed the ATV into a performance realm where an understanding of dynamics became essential, said Samuel Triest, a second-year master’s student in robotics. The team’s tests were performed at a site near Pittsburgh that CMU’s National Robotics Engineering Center uses to test autonomous off-road vehicles. “We were forcing the human to go through the same control interface as the robot would,” Wang said.