SUTD researchers have developed a reinforcement-learning-based safety system that teaches a stair-traversing service robot to brace itself mid-fall, addressing one of the biggest barriers to deploying autonomous robots on staircases.

Staircases are among the most challenging terrains a mobile robot can face. A multi-year field study found that robots designed for stair traversal fail at least 35 times more often on stairs than on level ground. The consequences can be significant. A robot that loses balance on a step accumulates momentum as it tumbles, threatening severe damage to itself, the building, and anyone in its path.

The paper is published in the journal Results in Engineering.