“A fall can potentially cause detrimental damage to the robot and enormous cost to repair,” said Sehoon Ha, PhD graduate from Georgia Tech. “Our work unified existing research about how to teach robots to fall by giving them a tool to automatically determine the total number of contacts, the order of contacts, and the position and timing of those contacts. All of that impacts the potential of a fall and changes the robot’s response.”
Ha has built upon Professor Karen Liu’s previous research that studied how cats modify their bodies in the midst of a fall. Liu knew from that work that one of the most important factors in a fall is the angle of the landing.
“From previous work, we knew a robot had the computational know-how to achieve a softer landing, but it didn’t have the hardware to move quickly enough like a cat,” Liu said. “Our new planning algorithm takes into account the hardware constraints and the capabilities of the robot, and suggests a sequence of contacts so the robot gradually can slow itself down.”
