How to fall gracefully if you’re a robot

October 26, 2015

Georgia Tech | Algorithm allows robot to fall gracefully

Researchers at Georgia Tech are teaching robots how to fall with grace and without serious damage.

This is becoming important as costly robots become more common in manufacturing, healthcare, and domestic tasks.

Ph.D. graduate Sehoon Ha and Professor Karen Liu developed a new algorithm that tells a robot how to react to a wide variety of falls, from a single step to recover from a gentle nudge to a rolling motion that breaks a high-speed fall. The idea is learn the best sequence of movements to slow their momentum and minimize the damage or injury they might cause to themselves or others while falling.

“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 (how many hands shoved it, for example), the order of contacts, and the position and timing of those contacts,” said Ha, now a postdoctoral associate at Disney Research Pittsburgh. “All of that impacts the potential of a fall and changes the robot’s response.”

The algorithm was validated in physics simulation and experimentally tested on a BioloidGP humanoid.

With the latest finding, Ha builds upon 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.”

They suggest robots may soon fall more gracefully than people — and possibly even cats.

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Abstract of Multiple Contact Planning for Minimizing Damage of Humanoid Falls

This paper introduces a new planning algorithm to minimize the damage of humanoid falls by utilizing multiple contact points. Given an unstable initial state of the robot, our approach plans for the optimal sequence of contact points such that the initial momentum is dissipated with minimal impacts on the robot. Instead of switching among a collection of individual control strategies, we propose a general algorithm which plans for appropriate responses to a wide variety of falls, from a single step to recover a gentle nudge, to a rolling motion to break a high-speed fall. Our algorithm transforms the falling problem into a sequence of inverted pendulum problems and use dynamic programming to solve the optimization efficiently. The planning algorithm is validated in physics simulation and experimentally tested on a BioloidGP humanoid.